Dissertations / Theses on the topic 'Latent variable path analysis'
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1
Roodman, Allison A. "A Test of a Model of Sexual Victimization: A Latent Variable Path Analysis." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/30102.
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Both a recent narrative review and a meta-analytic review of prevalence rates, indicates that prior sexual victimization increases risk for future victimization (Messman & Long, 1996, Roodman & Clum, in press). The purpose of this study was to examine two competing models of sexual victimization that examined the path between child abuse and later sexual victimization. Hypothesized mediating variables were negative cognitive schemas, dissociation, risky behaviors, and coping strategies. Structural equation modeling was used to examine two competing models of sexual victimization. A sample of 276 college students taking introductory psychology were participants. They anonymously completed a packet of questionnaires that provided the indicator variables for the path models that were tested.
Both models tested received minimal support but many of the proposed pathways in the model were not statistically significant suggesting problems with the models. Due to measurement issues with the manifest indicators of the latent factors, any results should be viewed with caution. It appears as though none of the factors in the model mediate the relationship between early and later victimization. However, both models tested demonstrated significant pathways between the factor for child abuse (comprising physical and sexual abuse) and negative cognitive schemas and for child abuse and dissociation. However, the paths from negative cognitive schemas and dissociation to sexual victimization (comprising both adolescent and adult sexual victimization) were not significant suggesting that, although these factors are influenced by child abuse, they do not mediate revictimization. Risky behaviors, as measured by consensual sex and alcohol consumption, do not appear to be influenced by early abuse, but there was a significant pathway between this factor and sexual victimization suggesting that these risky behaviors are independent risk factors for sexual victimization in adolescence and adulthood. In one model there was a significant pathway between child abuse and sexual victimization which is what would be expected given previous findings that suggest past abuse is the best predictor of future victimization experiences (Roodman & Clum, in press). That the other model did not demonstrate this relationship was surprising.Ph. D.
2
Paolucci, Elizabeth Oddone. "A latent variable path analysis of the development of pedophilia and its associated pathologies." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0010/NQ31058.pdf.
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Langley, Audra Kae. "Coping Efforts and Efficacy, Acculturation, and Post-Traumatic Symptomatology in Adolescents following Wildfire: A Latent Variable Path Analysis." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/26473.
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Recent studies of children and adolescents who have experienced a residential, industrial, or wild fire have suggested a causal link between fire disaster and PTSD related psychological distress. Not everyone, however, is equally affected by the stress of experiencing such an event, and the role of coping in this process may be an important mediating factor. Additionally, several studies have found that girls and African Americans report more distress following disasters than do boys and Caucasians. The current study sought to investigate the roles of exposure/loss, coping efficacy, and coping strategy in mediating psychological distress in adolescents after a disaster.
The current study included a representative sample of 206 9th graders from a Central Florida High School affected by severe wildfires who were assessed via self-report measures 3- and 10- months after the fires, in a latent variable path analysis to assess the fit of a model including exposure/loss, coping efficacy, coping strategy, and PTSD, depression, and anxiety scores. Moreover, acculturation level and SES were included along with gender and ethnicity in testing for the moderating role of sociodemographics, as little research has delved into the important proximal factors affecting reported racial differences, as ethnicity is better conceptualized as a distal variable that works through a variety of proximal variables to affect outcomes.
Results indicated that although the assessment of the global fit of the latent variable path model revealed it to be a poor fit to the data, component fit of the model pointed to a possible mediating role of coping efficacy between exposure/loss and psychological distress, as well as coping efficacy being associated negatively with avoidant coping strategies. Likewise, post hoc regression analyses indicated an important role for exposure/loss, coping efficacy, and coping strategy as they related to PTSD symptomatology in adolescents at both Time 1 and Time 2. Finally, although relationships between the proposed variables and PTSD did not interact with gender, acculturation, SES, or ethnicity, there was a significant interaction between acculturation and ethnicity signifying that for African American youth, high acculturation levels were predictive of less PTSD symptomatology.Ph. D.
4
Burnham, Alison J. "Multivariate latent variable regression : modelling and estimation /." *McMaster only, 1997.
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Christmas, Jacqueline. "Robust spatio-temporal latent variable models." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3051.
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Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.
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Hollis, R. Benjamin. "Mind wandering and online learning| A latent variable analysis." Thesis, Kent State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3618884.
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Thoughts drift in everyday life and in the classroom. The goal of this study was to investigate how often students reported off-task thinking while watching online lectures. These findings were related to working memory capacity, topic interest, and achievement goal orientations. Structural equation modeling was used to evaluate how all of these factors were related and predicted performance in the course.
In the presented findings, 126 participants completed three complex span tasks, answered a 2x2 goal orientation questionnaire, responded to eight mind-wandering probes while watching two online lectures, and rated interest in the lecture topics.
In the reported models, higher levels of mind wandering predicted lower levels of academic performance. Lower levels of working memory capacity predicted higher levels of mind wandering and lower levels of academic performance. Higher levels of topic interest predicted lower levels of mind wandering. Higher levels of mastery approach orientations (those who learn to master content) predicted higher levels of task-related interference. A novel mind wandering probe, thinking about or using another technology, accounted for 29% of off-task thinking. Implications of these findings and considerations for future research are discussed.
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Hollis, R. Benjamin. "Mind Wandering and Online Learning: A Latent Variable Analysis." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1385032513.
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Rastelli, Riccardo, and Nial Friel. "Optimal Bayesian estimators for latent variable cluster models." Springer Nature, 2018. http://dx.doi.org/10.1007/s11222-017-9786-y.
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In cluster analysis interest lies in probabilistically
capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share similar attributes or relational profiles. Bayesian posterior samples for the latent allocation variables can be effectively obtained in a wide range of clustering models, including finite mixtures, infinite mixtures, hidden Markov models and block models for networks. However, due to the categorical nature of the clustering variables and the lack of scalable algorithms, summary tools that can interpret such samples are not available. We adopt a Bayesian decision theoretical approach to define an optimality criterion for clusterings and propose a fast and context-independent greedy algorithm to find the best allocations. One important facet of our approach is that the optimal number of groups is automatically selected, thereby solving the clustering and the model-choice problems at the same time. We consider several loss functions to compare partitions and show that our approach can accommodate a wide range of cases. Finally, we illustrate our approach on both artificial and real datasets for three different clustering models: Gaussian mixtures, stochastic block models and latent block models for networks.
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Ridall, Peter Gareth. "Bayesian Latent Variable Models for Biostatistical Applications." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/16164/1/Peter_Ridall_Thesis.pdf.
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In this thesis we develop several kinds of latent variable models in order to address
three types of bio-statistical problem. The three problems are the treatment
effect of carcinogens on tumour development, spatial interactions between plant
species and motor unit number estimation (MUNE). The three types of data looked at are: highly heterogeneous longitudinal count data, quadrat counts of species on a rectangular lattice and lastly, electrophysiological data consisting
of measurements of compound muscle action potential (CMAP) area and amplitude.
Chapter 1 sets out the structure and the development of ideas presented
in this thesis from the point of view of: model structure, model selection, and
efficiency of estimation. Chapter 2 is an introduction to the relevant literature
that has in influenced the development of this thesis. In Chapter 3 we use the EM
algorithm for an application of an autoregressive hidden Markov model to describe
longitudinal counts. The data is collected from experiments to test the
effect of carcinogens on tumour growth in mice. Here we develop forward and
backward recursions for calculating the likelihood and for estimation. Chapter 4
is the analysis of a similar kind of data using a more sophisticated model, incorporating
random effects, but estimation this time is conducted from the Bayesian
perspective. Bayesian model selection is also explored. In Chapter 5 we move
to the two dimensional lattice and construct a model for describing the spatial
interaction of tree types. We also compare the merits of directed and undirected
graphical models for describing the hidden lattice. Chapter 6 is the application
of a Bayesian hierarchical model (MUNE), where the latent variable this time is
multivariate Gaussian and dependent on a covariate, the stimulus. Model selection
is carried out using the Bayes Information Criterion (BIC). In Chapter 7 we
approach the same problem by using the reversible jump methodology (Green,
1995) where this time we use a dual Gaussian-Binary representation of the latent
data. We conclude in Chapter 8 with suggestions for the direction of new
work. In this thesis, all of the estimation carried out on real data has only been
performed once we have been satisfied that estimation is able to retrieve the parameters
from simulated data.
Keywords: Amyotrophic lateral sclerosis (ALS), carcinogens, hidden Markov
models (HMM), latent variable models, longitudinal data analysis, motor unit
disease (MND), partially ordered Markov models (POMMs), the pseudo auto-
logistic model, reversible jump, spatial interactions.
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Ridall, Peter Gareth. "Bayesian Latent Variable Models for Biostatistical Applications." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16164/.
Full textAbstract:
In this thesis we develop several kinds of latent variable models in order to address three types of bio-statistical problem. The three problems are the treatment effect of carcinogens on tumour development, spatial interactions between plant species and motor unit number estimation (MUNE). The three types of data looked at are: highly heterogeneous longitudinal count data, quadrat counts of species on a rectangular lattice and lastly, electrophysiological data consisting of measurements of compound muscle action potential (CMAP) area and amplitude. Chapter 1 sets out the structure and the development of ideas presented in this thesis from the point of view of: model structure, model selection, and efficiency of estimation. Chapter 2 is an introduction to the relevant literature that has in influenced the development of this thesis. In Chapter 3 we use the EM algorithm for an application of an autoregressive hidden Markov model to describe longitudinal counts. The data is collected from experiments to test the effect of carcinogens on tumour growth in mice. Here we develop forward and backward recursions for calculating the likelihood and for estimation. Chapter 4 is the analysis of a similar kind of data using a more sophisticated model, incorporating random effects, but estimation this time is conducted from the Bayesian perspective. Bayesian model selection is also explored. In Chapter 5 we move to the two dimensional lattice and construct a model for describing the spatial interaction of tree types. We also compare the merits of directed and undirected graphical models for describing the hidden lattice. Chapter 6 is the application of a Bayesian hierarchical model (MUNE), where the latent variable this time is multivariate Gaussian and dependent on a covariate, the stimulus. Model selection is carried out using the Bayes Information Criterion (BIC). In Chapter 7 we approach the same problem by using the reversible jump methodology (Green, 1995) where this time we use a dual Gaussian-Binary representation of the latent data. We conclude in Chapter 8 with suggestions for the direction of new work. In this thesis, all of the estimation carried out on real data has only been performed once we have been satisfied that estimation is able to retrieve the parameters from simulated data. Keywords: Amyotrophic lateral sclerosis (ALS), carcinogens, hidden Markov models (HMM), latent variable models, longitudinal data analysis, motor unit disease (MND), partially ordered Markov models (POMMs), the pseudo auto- logistic model, reversible jump, spatial interactions.
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Dean, Nema. "Variable selection and other extensions of the mixture model clustering framework /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8943.
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Lohman, Matthew. "Frailty and Depression: A Latent Trait Analysis." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3324.
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Background: Frailty, a state indicating vulnerability to poor health outcomes, is a common condition in later life. However, research and intervention progress is hindered by the current lack of a consensus frailty definition and poor understanding of relationships between frailty and depression. Objectives: The goal of this research is to understand the interrelationships between frailty and depression among older adults. Specifically, this project aims 1) to examine the construct overlap between depression and three definitions of frailty (biological syndrome, medical burdens, and functional domains), 2) to determine the degree to which this overlap varies by age, gender, race/ethnicity and other individual characteristics, 3) to evaluate how the association between frailty and depression influences prediction of adverse health outcomes. Methods: This project uses data from the 2004-2012 Health and Retirement Study (HRS), an ongoing, nationally-representative cohort study of adults over the age of 55. Frailty was indexed by three alternative conceptual models: 1) biological syndrome, 2) cumulative medical burdens, and 3) functional domains. Depressive symptoms were indexed by the 8-item Center for Epidemiologic Studies Depression (CESD) scale. Latent class analysis and confirmatory factor analysis were used to assess the construct overlap between depressive symptoms and frailty. Latent growth curve modeling were used to evaluate associations between frailty and depression, and to estimate their joint influence on two adverse health outcomes: nursing home admission and falls. Results: The measurement overlap of frailty and depression was high using a categorical latent variable approach. Approximately 73% of individuals with severe depressive symptoms, and 85% of individuals with primarily somatic depressive symptoms, were categorized as concurrently frail. When modeled as continuous latent factors, each of the three frailty latent factors was significantly correlated with depression: biological syndrome (ρ = .67, p <.01); functional domains (ρ = .70, p <.01); and medical burdens (ρ = .62, p <.01). Higher latent frailty trajectories were associated with higher likelihood of experiencing nursing home admission and serious falls. This association with adverse health outcomes was attenuated after adjustment for depression as a time-varying covariate. Conclusions: Findings suggest that frailty and frailty trajectories are potentially important indicators of vulnerability to adverse health outcomes. Future investigations of frailty syndrome, however it is operationalized, should account for its substantial association with depression in order to develop more accurate measurement and effective treatment.
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Flores-Cerrillo, Jesus MacGregor John F. "Quality control for batch processes using multivariate latent variable methods /." *McMaster only, 2003.
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Xue, Jianhong. "What drives a knowledge-based industry to cluster? : a latent variable analysis /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426116.
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Foxall, Robert John. "Likelihood analysis of the multi-layer perceptron and related latent variable models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327211.
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Kelly, Lesly Ann. "Nursing Surveillance in the Acute Care Setting: Latent Variable Development and Analysis." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/193636.
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The nursing profession has utilized a variety of terms to describe the work that nurses do, such as observing, monitoring, and critical thinking. Nursing surveillance is a term emerging in the research and clinical environment to describe the care, both seen and unseen, by professional registered nurses. It has been described as a complex, multi-dimensional concept that influences patient outcomes, yet little research has been done to examine the concept, how it is measured, and its role in outcomes.The surveillance process includes ongoing data collection, interpretation, and synthesis for decision making. This research proposes that nursing surveillance is comprised of five dimensions: actions, expertise, early recognition, intuition, and decision making. The purpose of this study is to examine the dimensions of nursing surveillance in the acute care setting.This study used a descriptive design to survey nurses on the dimensions of nursing surveillance. The survey consisted of four existing instruments measuring expertise, early recognition, intuition, and decision making, and one new instrument measuring activities associated with nursing surveillance. A content review panel was used to develop the new Nursing Surveillance Activities Scale. A sample of 158 medical-surgical nurses participated in completing the full Nursing Surveillance Survey.The goal of the analysis was to determine how well the dimensions represented the surveillance variable; however, based on sample size, revisions to the methods were made. Factor analysis was used to analyze each instrument's items and total representation of the variable. The instruments performed adequately in psychometric testing, and modifications were made so composite development could be achieved. The dimensions were factored as a composite variable and four of the five dimensions loaded onto a single variable, while the activities dimensions loaded separately. These results can be explained through a theoretical difference between the dimensions or limitations with the newly created Nursing Surveillance Activities Scale.This study identified a relationship between the four cognitive dimensions of nursing surveillance and their representation of the variable. Future research in nursing surveillance should analyze the role of the nursing surveillance variable, including the relationship to nursing outcomes.
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Ren, Chunfeng. "LATENT VARIABLE MODELS GIVEN INCOMPLETELY OBSERVED SURROGATE OUTCOMES AND COVARIATES." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3473.
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Latent variable models (LVMs) are commonly used in the scenario where the outcome of the main interest is an unobservable measure, associated with multiple observed surrogate outcomes, and affected by potential risk factors. This thesis develops an approach of efficient handling missing surrogate outcomes and covariates in two- and three-level latent variable models. However, corresponding statistical methodologies and computational software are lacking efficiently analyzing the LVMs given surrogate outcomes and covariates subject to missingness in the LVMs. We analyze the two-level LVMs for longitudinal data from the National Growth of Health Study where surrogate outcomes and covariates are subject to missingness at any of the levels. A conventional method for efficient handling of missing data is to reexpress the desired model as a joint distribution of variables, including the surrogate outcomes that are subject to missingness conditional on all of the covariates that are completely observable, and estimate the joint model by maximum likelihood, which is then transformed to the desired model. The joint model, however, identifies more parameters than desired, in general. The over-identified joint model produces biased estimates of LVMs so that it is most necessary to describe how to impose constraints on the joint model so that it has a one-to-one correspondence with the desired model for unbiased estimation. The constrained joint model handles missing data efficiently under the assumption of ignorable missing data and is estimated by a modified application of the expectation-maximization (EM) algorithm.
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Löfstedt, Tommy. "OnPLS : Orthogonal projections to latent structures in multiblock and path model data analysis." Doctoral thesis, Umeå universitet, Kemiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-55438.
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The amounts of data collected from each sample of e.g. chemical or biological materials have increased by orders of magnitude since the beginning of the 20th century. Furthermore, the number of ways to collect data from observations is also increasing. Such configurations with several massive data sets increase the demands on the methods used to analyse them. Methods that handle such data are called multiblock methods and they are the topic of this thesis. Data collected from advanced analytical instruments often contain variation from diverse mutually independent sources, which may confound observed patterns and hinder interpretation of latent variable models. For this reason, new methods have been developed that decompose the data matrices, placing variation from different sources of variation into separate parts. Such procedures are no longer merely pre-processing filters, as they initially were, but have become integral elements of model building and interpretation. One strain of such methods, called OPLS, has been particularly successful since it is easy to use, understand and interpret. This thesis describes the development of a new multiblock data analysis method called OnPLS, which extends the OPLS framework to the analysis of multiblock and path models with very general relationships between blocks in both rows and columns. OnPLS utilises OPLS to decompose sets of matrices, dividing each matrix into a globally joint part (a part shared with all the matrices it is connected to), several locally joint parts (parts shared with some, but not all, of the connected matrices) and a unique part that no other matrix shares. The OnPLS method was applied to several synthetic data sets and data sets of “real” measurements. For the synthetic data sets, where the results could be compared to known, true parameters, the method generated global multiblock (and path) models that were more similar to the true underlying structures compared to models without such decompositions. I.e. the globally joint, locally joint and unique models more closely resembled the corresponding true data. When applied to the real data sets, the OnPLS models revealed chemically or biologically relevant information in all kinds of variation, effectively increasing the interpretability since different kinds of variation are distinguished and separately analysed. OnPLS thus improves the quality of the models and facilitates better understanding of the data since it separates and separately analyses different kinds of variation. Each kind of variation is purer and less tainted by other kinds. OnPLS is therefore highly recommended to anyone engaged in multiblock or path model data analysis.
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Scruby, Gavin John. "Statistical and neural network techniques for independent component analysis and latent variable applications." Thesis, Royal Holloway, University of London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313402.
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Chao, Yi. "Bayesian Hierarchical Latent Model for Gene Set Analysis." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/32060.
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Pathway is a set of genes which are predefined and serve a particular celluar or physiological function. Ranking pathways relevant to a particular phenotype can help researchers focus on a few sets of genes in pathways. In this thesis, a Bayesian hierarchical latent model was proposed using generalized linear random effects model. The
advantage of the approach was that it can easily incorporate prior knowledges when the sample size was small and the number of genes was large. For the covariance matrix of a set of random variables, two Gaussian random processes were considered to construct the dependencies among genes in a pathway. One was based on the polynomial kernel and the other was based on the Gaussian kernel. Then these two kernels were compared with constant covariance matrix of the random effect by using the ratio, which was based on the joint posterior distribution with respect to each model. For
mixture models, log-likelihood values were computed at different values of the mixture proportion, compared among mixtures of selected kernels and point-mass density (or constant covariance matrix). The approach was applied to a data set (Mootha et al., 2003) containing the expression profiles of type II diabetes where the motivation was to identify pathways that can discriminate between normal patients and patients with type II diabetes.Master of Science
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Farouni, Tarek. "Latent Variable Models of Categorical Responses in the Bayesian and Frequentist Frameworks." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1412374136.
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Abonyi, J., FD Tamás, S. Potgieter, and H. Potgieter. "Analysis of Trace Elements in South African Clinkers using Latent Variable Model and Clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000893.
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The trace element content of clinkers (and possibly of cements) can be used to identify the manufacturing factory. The Mg, Sr, Ba,
Mn, Ti, Zr, Zn and V content of clinkers give detailed information for the determination of the origin of clinkers produced in
different factories. However, for the analysis of such complex data there is a need for algorithmic tools for the visualization and
clustering of the samples. This paper proposes a new approach for this purpose. The analytical data are transformed into a twodimensional
latent space by factor analysis (probabilistic principal component analysis) and dendograms are constructed for
cluster formation. The classification of South African clinkers is used as an illustrative example for the approach.
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Abonyia, J., FD Tamas, and S. Potgieter. "Analysis of trace elements in South African clinkers using latent variable model and clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001952.
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Abstract
The trace element content of clinkers (and possibly of cements) can be used to identify the manufacturing factory. The Mg, Sr, Ba,
Mn, Ti, Zr, Zn and V content of clinkers give detailed information for the determination of the origin of clinkers produced in
different factories. However, for the analysis of such complex data there is a need for algorithmic tools for the visualization and
clustering of the samples. This paper proposes a new approach for this purpose. The analytical data are transformed into a twodimensional
latent space by factor analysis (probabilistic principal component analysis) and dendograms are constructed for
cluster formation. The classification of South African clinkers is used as an illustrative example for the approach.
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Hafez, Mai. "Analysis of multivariate longitudinal categorical data subject to nonrandom missingness : a latent variable approach." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3184/.
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Longitudinal data are collected for studying changes across time. In social sciences, interest is often in theoretical constructs, such as attitudes, behaviour or abilities, which cannot be directly measured. In that case, multiple related manifest (observed) variables, for example survey questions or items in an ability test, are used as indicators for the constructs, which are themselves treated as latent (unobserved) variables. In this thesis, multivariate longitudinal data is considered where multiple observed variables, measured at each time point, are used as indicators for theoretical constructs (latent variables) of interest. The observed items and the latent variables are linked together via statistical latent variable models. A common problem in longitudinal studies is missing data, where missingness can be classiffed into one of two forms. Dropout occurs when subjects exit the study prematurely, while intermittent missingness takes place when subjects miss one or more occasions but show up on a subsequent wave of the study. Ignoring the missingness mechanism can lead to biased estimates, especially when the missingness is nonrandom. The approach proposed in this thesis uses latent variable models to capture the evolution of a latent phenomenon over time, while incorporating a missingness mechanism to account for possibly nonrandom forms of missingness. Two model specifications are presented, the first of which incorporates dropout only in the missingness mechanism, while the other accounts for both dropout and intermittent missingness allowing them to be informative by being modelled as functions of the latent variables and possibly observed covariates. Models developed in this thesis consider ordinal and binary observed items, because such variables are often met in social surveys, while the underlying latent variables are assumed to be continuous. The proposed models are illustrated by analysing people's perceptions on women's work using three questions from five waves of the British Household Panel Survey.
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Chen, Hongshu. "Sampling-based Bayesian latent variable regression methods with applications in process engineering." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1189650596.
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Orriols, Majoral Xavier. "Generative Models for Video Analysis and 3D Range Data Applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2004. http://hdl.handle.net/10803/3037.
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La mayoría de problemas en Visión por computador no contienen una relación directa entre el estímulo que proviene de sensores de tipo genérico y su correspondiente categoría perceptual. Este tipo de conexión requiere de una tarea de aprendizaje compleja. De hecho, las formas básicas de energía, y sus posibles combinaciones, son un número reducido en comparación a las infinitas categorías perceptuales correspondientes a objetos, acciones, relaciones entre objetos, etc. Dos factores principales determinan el nivel de dificultad de cada problema específico: i) los diferentes niveles de información que se utilizan, y ii) la complejidad del modelo que se emplea con el objetivo de explicar las observaciones. La elección de una representación adecuada para los datos toma una relevancia significativa cuando se tratan invariancias, dado que estas siempre implican una reducción del los grados de libertad del sistema, i.e., el número necesario de coordenadas para la representación es menor que el empleado en la captura de datos. De este modo, la descomposición en unidades básicas y el cambio de representación dan lugar a que un problema complejo se pueda transformar en uno de manejable. Esta simplificación del problema de la estimación debe depender del mecanismo propio de combinación de estas primitivas con el fin de obtener una descripción óptima del modelo complejo global. Esta tesis muestra como los Modelos de Variables Latentes reducen dimensionalidad, que teniendo en cuenta las simetrías internas del problema, ofrecen una manera de tratar con datos parciales y dan lugar a la posibilidad de predicciones de nuevas observaciones.
Las líneas de investigación de esta tesis están dirigidas al manejo de datos provinentes de múltiples fuentes. Concretamente, esta tesis presenta un conjunto de nuevos algoritmos aplicados a dos áreas diferentes dentro de la Visión por Computador: i) video análisis y sumarización y ii) datos range 3D. Ambas áreas se han enfocado a través del marco de los Modelos Generativos, donde se han empleado protocolos similares para representar datos.
The majority of problems in Computer Vision do not contain a direct relation between the stimuli provided by a general purpose sensor and its corresponding perceptual category. A complex learning task must be involved in order to provide such a connection. In fact, the basic forms of energy, and their possible combinations are a reduced number compared to the infinite possible perceptual categories corresponding to objects, actions, relations among objects... Two main factors determine the level of difficulty of a specific problem: i) The different levels of information that are employed and ii) The complexity of the model that is intended to explain the observations.
The choice of an appropriate representation for the data takes a significant relevance when it comes to deal with invariances, since these usually imply that the number of intrinsic degrees of
freedom in the data distribution is lower than the coordinates used to represent it. Therefore, the decomposition into basic units (model parameters) and the change of representation, make that a complex problem can be transformed into a manageable one. This simplification of the estimation problem has to rely on a proper mechanism of combination of those primitives in order to give an optimal description of the global complex model. This thesis shows how Latent Variable Models reduce dimensionality, taking into account the internal symmetries of a problem, provide a manner of dealing with missing data and make possible predicting new observations.
The lines of research of this thesis are directed to the management of multiple data sources. More specifically, this thesis presents a set of new algorithms applied to two different areas in Computer Vision: i) video analysis and summarization, and ii) 3D range data. Both areas have been approached through the Generative Models framework, where similar protocols for representing data have been employed.
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Grün, Bettina, and Gertraud Malsiner-Walli. "Bayesian Latent Class Analysis with Shrinkage Priors: An Application to the Hungarian Heart Disease Data." FedOA -- Federico II University Press, 2018. http://epub.wu.ac.at/6612/1/heart.pdf.
Full textAbstract:
Latent class analysis explains dependency structures in multivariate categorical
data by assuming the presence of latent classes. We investigate the specification of suitable
priors for the Bayesian latent class model to determine the number of classes and perform
variable selection. Estimation is possible using standard tools implementing general purpose
Markov chain Monte Carlo sampling techniques such as the software JAGS. However, class
specific inference requires suitable post-processing in order to eliminate label switching. The
proposed Bayesian specification and analysis method is applied to the Hungarian heart disease
data set to determine the number of classes and identify relevant variables and results are
compared to those obtained with the standard prior for the component specific parameters.
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Ben, Boubaker Moez, and Boubaker Moez Ben. "Non-destructive quality control of carbon anodes using modal analysis, acousto-ultrasonic and latent variable methods." Doctoral thesis, Université Laval, 2017. http://hdl.handle.net/20.500.11794/27843.
Full textAbstract:
La performance des cuves d’électrolyse utilisées dans la production d’aluminium primaire par le procédé Hall-Héroult est fortement influencée par la qualité des anodes de carbone. Celles-ci sont de plus en plus variables en raison de la qualité décroissante des matières premières (coke et braie) et des changements de fournisseurs qui deviennent de plus en plus fréquents afin de réduire le coût d’achat et de rencontrer les spécifications des usines. En effet, les défauts des anodes, tels les fissures, les pores et les hétérogénéités, causés par cette variabilité, doivent être détectés le plus tôt possible afin d’éviter d’utiliser des anodes défectueuses dans les cuves et/ou d’apporter des ajustements au niveau du procédé de fabrication des anodes. Cependant, les fabricants d’anodes ne sont pas préparés pour réagir à cette situation afin de maintenir une qualité d'anode stable. Par conséquent, il devient prioritaire de développer des techniques permettant d’inspecter le volume complet de chaque anode individuelle afin d’améliorer le contrôle de la qualité des anodes et de compenser la variabilité provenant des matières premières. Un système d’inspection basé sur les techniques d’analyse modale et d’acousto-ultrasonique est proposé pour contrôler la qualité des anodes de manière rapide et non destructive. Les données massives (modes de vibration et signaux acoustiques) ont été analysées à l'aide de méthodes statistiques à variables latentes, telles que l'Analyse en Composantes Principales (ACP) et la Projection sur les Structures Latentes (PSL), afin de regrouper les anodes testées en fonction de leurs signatures vibratoires et acousto-ultrasoniques. Le système d'inspection a été premièrement investigué sur des tranches d'anodes industrielles et ensuite testé sur plusieurs anodes pleine grandeur produites sous différentes conditions à l’usine de Alcoa Deschambault au Québec (ADQ). La méthode proposée a permis de distinguer les anodes saines de celles contenant des défauts ainsi que d’identifier le type et la sévérité des défauts, et de les localiser. La méthode acousto-ultrasonique a été validée qualitativement par la tomographie à rayon-X, pour les analyses des tranches d’anodes. Pour les tests réalisés sur les blocs d’anode, la validation a été réalisée au moyen de photos recueillies après avoir coupé certaines anodes parmi celles testées.La performance des cuves d’électrolyse utilisées dans la production d’aluminium primaire par le procédé Hall-Héroult est fortement influencée par la qualité des anodes de carbone. Celles-ci sont de plus en plus variables en raison de la qualité décroissante des matières premières (coke et braie) et des changements de fournisseurs qui deviennent de plus en plus fréquents afin de réduire le coût d’achat et de rencontrer les spécifications des usines. En effet, les défauts des anodes, tels les fissures, les pores et les hétérogénéités, causés par cette variabilité, doivent être détectés le plus tôt possible afin d’éviter d’utiliser des anodes défectueuses dans les cuves et/ou d’apporter des ajustements au niveau du procédé de fabrication des anodes. Cependant, les fabricants d’anodes ne sont pas préparés pour réagir à cette situation afin de maintenir une qualité d'anode stable. Par conséquent, il devient prioritaire de développer des techniques permettant d’inspecter le volume complet de chaque anode individuelle afin d’améliorer le contrôle de la qualité des anodes et de compenser la variabilité provenant des matières premières. Un système d’inspection basé sur les techniques d’analyse modale et d’acousto-ultrasonique est proposé pour contrôler la qualité des anodes de manière rapide et non destructive. Les données massives (modes de vibration et signaux acoustiques) ont été analysées à l'aide de méthodes statistiques à variables latentes, telles que l'Analyse en Composantes Principales (ACP) et la Projection sur les Structures Latentes (PSL), afin de regrouper les anodes testées en fonction de leurs signatures vibratoires et acousto-ultrasoniques. Le système d'inspection a été premièrement investigué sur des tranches d'anodes industrielles et ensuite testé sur plusieurs anodes pleine grandeur produites sous différentes conditions à l’usine de Alcoa Deschambault au Québec (ADQ). La méthode proposée a permis de distinguer les anodes saines de celles contenant des défauts ainsi que d’identifier le type et la sévérité des défauts, et de les localiser. La méthode acousto-ultrasonique a été validée qualitativement par la tomographie à rayon-X, pour les analyses des tranches d’anodes. Pour les tests réalisés sur les blocs d’anode, la validation a été réalisée au moyen de photos recueillies après avoir coupé certaines anodes parmi celles testées.
The performance of the Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting is strongly influenced by the quality of carbon anodes, particularly by the presence of defects in their internal structure, such as cracks, pores and heterogeneities. This is partly due to the decreasing quality and increasing variability of the raw materials available on the market as well as the frequent suppliers changes made in order to meet the smelter’s specifications and to reduce purchasing costs. However, the anode producers are not prepared to cope with these variations and in order to maintain consistent anode quality. Consequently, it becomes a priority to develop alternative methods for inspecting each anode block to improve quality control and maintain consistent anode quality in spite of the variability of incoming raw materials.A rapid and non-destructive inspection system for anode quality control is proposed based on modal analysis and acousto-ultrasonic techniques. The large set of vibration and acousto-ultrasonic data collected from baked anode materials was analyzed using multivariate latent variable methods, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), in order to cluster the tested anodes based on vibration and their acousto-ultrasonic signatures. The inspection system was investigated first using slices collected from industrial anodes and then on several full size anodes produced under different conditions at the Alcoa Deschambault in Québec (ADQ). It is shown that the proposed method allows discriminating defect-free anodes from those containing various types of defects. In addition, the acousto-ultrasonic features obtained in different frequency ranges were found to be sensitive to the defects severities and were able to locate them in anode blocks. The acousto-ultrasonic method was validated qualitatively using X-ray computed tomography, when studying the anode slices. The results obtained on the full size anode blocks were validated by means of images collected after cutting some tested anodes.
The performance of the Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting is strongly influenced by the quality of carbon anodes, particularly by the presence of defects in their internal structure, such as cracks, pores and heterogeneities. This is partly due to the decreasing quality and increasing variability of the raw materials available on the market as well as the frequent suppliers changes made in order to meet the smelter’s specifications and to reduce purchasing costs. However, the anode producers are not prepared to cope with these variations and in order to maintain consistent anode quality. Consequently, it becomes a priority to develop alternative methods for inspecting each anode block to improve quality control and maintain consistent anode quality in spite of the variability of incoming raw materials.A rapid and non-destructive inspection system for anode quality control is proposed based on modal analysis and acousto-ultrasonic techniques. The large set of vibration and acousto-ultrasonic data collected from baked anode materials was analyzed using multivariate latent variable methods, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), in order to cluster the tested anodes based on vibration and their acousto-ultrasonic signatures. The inspection system was investigated first using slices collected from industrial anodes and then on several full size anodes produced under different conditions at the Alcoa Deschambault in Québec (ADQ). It is shown that the proposed method allows discriminating defect-free anodes from those containing various types of defects. In addition, the acousto-ultrasonic features obtained in different frequency ranges were found to be sensitive to the defects severities and were able to locate them in anode blocks. The acousto-ultrasonic method was validated qualitatively using X-ray computed tomography, when studying the anode slices. The results obtained on the full size anode blocks were validated by means of images collected after cutting some tested anodes.
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Todd, John B. (John Bruce). "A Path Analysis of Caregiving the Elderly: Voluntariness as a Variable of Role Assumption." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278760/.
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Structural equation modeling was utilized in studying the voluntariness of the assumption of caregiving status. A model hypothesizing the stress flow that occurs when assuming a new life schema was presented. Utilizing three groups of caregiving populations, Home Caregivers, Intermediate Care Facility Aides, and Intensive Care Units and Emergency Room Nurses (N = 66), measures were administered to determine the voluntariness of the assumption of the role of caregiver. Path analysis and causal interpretation were utilized to determine outcomes. The involuntary assumption of the role of caretaker was shown to significantly affect depression and burnout rates negatively when perceived feelings of burden were high. When caretaker age was greater upon assumption of the role, self-esteem was low and family support was perceived to be lacking. When the role of caretaker is assumed on a voluntary basis and support from outside sources is perceived as helpful (i.e., social or financial support from the family), job stress and the subjective manageability of the symptoms were viewed as manageable. Implications for those assuming the role of caretaker with the elderly were examined, and recommendations for further training and interventions within the caretaker population were offered.
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Bylesjö, Max. "Latent variable based computational methods for applications in life sciences : Analysis and integration of omics data sets." Doctoral thesis, Umeå universitet, Kemi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1616.
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With the increasing availability of high-throughput systems for parallel monitoring of multiple variables, e.g. levels of large numbers of transcripts in functional genomics experiments, massive amounts of data are being collected even from single experiments. Extracting useful information from such systems is a non-trivial task that requires powerful computational methods to identify common trends and to help detect the underlying biological patterns. This thesis deals with the general computational problems of classifying and integrating high-dimensional empirical data using a latent variable based modeling approach. The underlying principle of this approach is that a complex system can be characterized by a few independent components that characterize the systematic properties of the system. Such a strategy is well suited for handling noisy, multivariate data sets with strong multicollinearity structures, such as those typically encountered in many biological and chemical applications. The main foci of the studies this thesis is based upon are applications and extensions of the orthogonal projections to latent structures (OPLS) method in life science contexts. OPLS is a latent variable based regression method that separately describes systematic sources of variation that are related and unrelated to the modeling aim (for instance, classifying two different categories of samples). This separation of sources of variation can be used to pre-process data, but also has distinct advantages for model interpretation, as exemplified throughout the work. For classification cases, a probabilistic framework for OPLS has been developed that allows the incorporation of both variance and covariance into classification decisions. This can be seen as a unification of two historical classification paradigms based on either variance or covariance. In addition, a non-linear reformulation of the OPLS algorithm is outlined, which is useful for particularly complex regression or classification tasks. The general trend in functional genomics studies in the post-genomics era is to perform increasingly comprehensive characterizations of organisms in order to study the associations between their molecular and cellular components in greater detail. Frequently, abundances of all transcripts, proteins and metabolites are measured simultaneously in an organism at a current state or over time. In this work, a generalization of OPLS is described for the analysis of multiple data sets. It is shown that this method can be used to integrate data in functional genomics experiments by separating the systematic variation that is common to all data sets considered from sources of variation that are specific to each data set.Funktionsgenomik är ett forskningsområde med det slutgiltiga målet att karakterisera alla gener i ett genom hos en organism. Detta inkluderar studier av hur DNA transkriberas till mRNA, hur det sedan translateras till proteiner och hur dessa proteiner interagerar och påverkar organismens biokemiska processer. Den traditionella ansatsen har varit att studera funktionen, regleringen och translateringen av en gen i taget. Ny teknik inom fältet har dock möjliggjort studier av hur tusentals transkript, proteiner och små molekyler uppträder gemensamt i en organism vid ett givet tillfälle eller över tid. Konkret innebär detta även att stora mängder data genereras även från små, isolerade experiment. Att hitta globala trender och att utvinna användbar information från liknande data-mängder är ett icke-trivialt beräkningsmässigt problem som kräver avancerade och tolkningsbara matematiska modeller. Denna avhandling beskriver utvecklingen och tillämpningen av olika beräkningsmässiga metoder för att klassificera och integrera stora mängder empiriskt (uppmätt) data. Gemensamt för alla metoder är att de baseras på latenta variabler: variabler som inte uppmätts direkt utan som beräknats från andra, observerade variabler. Detta koncept är väl anpassat till studier av komplexa system som kan beskrivas av ett fåtal, oberoende faktorer som karakteriserar de huvudsakliga egenskaperna hos systemet, vilket är kännetecknande för många kemiska och biologiska system. Metoderna som beskrivs i avhandlingen är generella men i huvudsak utvecklade för och tillämpade på data från biologiska experiment. I avhandlingen demonstreras hur dessa metoder kan användas för att hitta komplexa samband mellan uppmätt data och andra faktorer av intresse, utan att förlora de egenskaper hos metoden som är kritiska för att tolka resultaten. Metoderna tillämpas för att hitta gemensamma och unika egenskaper hos regleringen av transkript och hur dessa påverkas av och påverkar små molekyler i trädet poppel. Utöver detta beskrivs ett större experiment i poppel där relationen mellan nivåer av transkript, proteiner och små molekyler undersöks med de utvecklade metoderna.
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Bylesjö, Max. "Latent variable based computational methods for applications in life sciences : Analysis and integration of omics data sets /." Umeå : Chemistry Kemi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1616.
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Warpe, Hrusheekesh Sunil. "An Analysis of EcoRouting Using a Variable Acceleration Rate Synthesis Model." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78678.
Full textAbstract:
Automotive manufacturers are facing increasing pressure from legislative bodies and consumers to reduce fuel consumption and greenhouse gas emissions of vehicles. This has led to many automotive manufacturers starting production of Plug-in Hybrid Electric Vehicles (PHEV's) and Battery Electric Vehicles (BEV's). Another method that helps to reduce the environmental effect of transportation is EcoRouting.
The standard Global Positioning System (GPS) navigation offers route alternatives between user specified origin and destination. This technology provides multiple routes to the user and focuses on reducing the travel time to reach to the destination. EcoRouting is the method to determine a route that minimizes vehicle energy consumption, unlike traditional routing methods that minimize travel time. An EcoRouting system has been developed as a part of this thesis that takes in information such as speed limits, the number of stop lights, and the road grade to calculate the energy consumption of a vehicle along a route.
A synthesis methodology is introduced that takes into consideration the distance between the origin and destination, the acceleration rate of the vehicle, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A new approach is presented in this thesis that weighs the energy consumption for different routes and chooses the route with the least energy consumption, subject to a constraint on travel time. A cost function for quantifying the effect of travel time is introduced that assists in choosing the EcoRoute with an acceptable limit on the travel time required to reach the destination.
The analysis of the EcoRouting system with minimum number of conditional stops and maximum number of conditional stops is done in this thesis. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. A sensitivity study is performed to observe the change in energy consumption of the vehicle with a change in acceleration rates and road grade. Three routing scenarios are presented in this thesis to demonstrate the functionality of EcoRouting. The EcoRouting model presented in this thesis is also validated against an external EcoRouting research paper and the energy consumption along three routes is calculated. The EcoRoute solution is found to vary with the information given to the variable acceleration rate model. The synthesis and the results that are obtained show that parameters such as acceleration, deceleration, and road grade affect the overall energy consumption of a vehicle and are helpful in determining the EcoRoute.Master of Science
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Katsikatsou, Myrsini. "Composite Likelihood Estimation for Latent Variable Models with Ordinal and Continuous, or Ranking Variables." Doctoral thesis, Uppsala universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-188342.
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The estimation of latent variable models with ordinal and continuous, or ranking variables is the research focus of this thesis. The existing estimation methods are discussed and a composite likelihood approach is developed. The main advantages of the new method are its low computational complexity which remains unchanged regardless of the model size, and that it yields an asymptotically unbiased, consistent, and normally distributed estimator. The thesis consists of four papers. The first one investigates the two main formulations of the unrestricted Thurstonian model for ranking data along with the corresponding identification constraints. It is found that the extra identifications constraints required in one of them lead to unreliable estimates unless the constraints coincide with the true values of the fixed parameters. In the second paper, a pairwise likelihood (PL) estimation is developed for factor analysis models with ordinal variables. The performance of PL is studied in terms of bias and mean squared error (MSE) and compared with that of the conventional estimation methods via a simulation study and through some real data examples. It is found that the PL estimates and standard errors have very small bias and MSE both decreasing with the sample size, and that the method is competitive to the conventional ones. The results of the first two papers lead to the next one where PL estimation is adjusted to the unrestricted Thurstonian ranking model. As before, the performance of the proposed approach is studied through a simulation study with respect to relative bias and relative MSE and in comparison with the conventional estimation methods. The conclusions are similar to those of the second paper. The last paper extends the PL estimation to the whole structural equation modeling framework where data may include both ordinal and continuous variables as well as covariates. The approach is demonstrated through an example run in R software. The code used has been incorporated in the R package lavaan (version 0.5-11).
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DeBusk-Lane, Morgan. "Variable- and Person-Centered Approaches to Examining Construct-Relevant Multidimensionality in Writing Self-Efficacy." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5938.
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Writing self-efficacy is a vital component to a students’ motivation and will to succeed towards writing. The measurement of writing self-efficacy over the past 40 years, despite its development, continues to largely be represented by Confirmatory Factor Analysis models that are limited due to their restricted item to factor constraints. These constraints, given prior literature and the theoretical understanding of self-efficacy, do not adequately model construct- relevant psychometric multidimensionality as a product of conceptual overlap or a hierarchical or general factor. Given this, the present study’s purpose was to examine the adapted Self-efficacy for Writing Scale (SEWS) for the presence of construct-relevant psychometric multidimensionality through a series of measurement model comparisons and person-centered approaches. Using a sample 1,466 8th, 9th, and 10th graders, a bifactor exploratory structural equation model was found to best represent the data and demonstrate that the SEWS exhibits both construct-relevant multidimensionality as a function of conceptual overlap and the presence of a hierarchical theme. Using factor scores derived from this model, latent profile analysis was conducted to further establish validity of the measurement model and examine how students disaggregate into groups based on their response trends of the SEWS. Three profiles emerged greatly differentiated by global writing self-efficacy, with obvious and substantively varying specific factor differences between profiles. Concurrent, divergent, and discriminant validity evidence was established through a series of analyses that assessed predictors and outcomes of the profiles (e.g. demographics, standardized writing assessments, grades). Theoretical and educator implications and avenues for future researcher were discussed.
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Hu, Zhiguang. "Binary latent variable modelling in the analysis of health data with multiple binary outcomes in an air pollution study in Hong Kong /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19588975.
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Hu, Zhiguang, and 胡志光. "Binary latent variable modelling in the analysis of health data with multiple binary outcomes in an air pollution study in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31237058.
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Hirk, Rainer, Kurt Hornik, and Laura Vana. "Multivariate Ordinal Regression Models: An Analysis of Corporate Credit Ratings." WU Vienna University of Economics and Business, 2017. http://epub.wu.ac.at/5389/1/Report132_lvana.pdf.
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Correlated ordinal data typically arise from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. We investigate how sensitive the pairwise likelihood estimates are to the number of subjects and to the presence of observations missing completely at random, and find that these estimates are robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US companies as well as an incomplete panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework.Series: Research Report Series / Department of Statistics and Mathematics
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Hirk, Rainer, Kurt Hornik, and Laura Vana. "Multivariate ordinal regression models: an analysis of corporate credit ratings." Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/s10260-018-00437-7.
Full textAbstract:
Correlated ordinal data typically arises from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal regression models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. Using simulated data sets with varying number of subjects, we investigate the performance of the pairwise likelihood estimates and find them to be robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US firms as well as an unbalanced panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework.
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Parekh, Sanjeel. "Learning representations for robust audio-visual scene analysis." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT015/document.
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L'objectif de cette thèse est de concevoir des algorithmes qui permettent la détection robuste d’objets et d’événements dans des vidéos en s’appuyant sur une analyse conjointe de données audio et visuelle. Ceci est inspiré par la capacité remarquable des humains à intégrer les caractéristiques auditives et visuelles pour améliorer leur compréhension de scénarios bruités. À cette fin, nous nous appuyons sur deux types d'associations naturelles entre les modalités d'enregistrements audiovisuels (réalisés à l'aide d'un seul microphone et d'une seule caméra), à savoir la corrélation mouvement/audio et la co-occurrence apparence/audio. Dans le premier cas, nous utilisons la séparation de sources audio comme application principale et proposons deux nouvelles méthodes dans le cadre classique de la factorisation par matrices non négatives (NMF). L'idée centrale est d'utiliser la corrélation temporelle entre l'audio et le mouvement pour les objets / actions où le mouvement produisant le son est visible. La première méthode proposée met l'accent sur le couplage flexible entre les représentations audio et de mouvement capturant les variations temporelles, tandis que la seconde repose sur la régression intermodale. Nous avons séparé plusieurs mélanges complexes d'instruments à cordes en leurs sources constituantes en utilisant ces approches.Pour identifier et extraire de nombreux objets couramment rencontrés, nous exploitons la co-occurrence apparence/audio dans de grands ensembles de données. Ce mécanisme d'association complémentaire est particulièrement utile pour les objets où les corrélations basées sur le mouvement ne sont ni visibles ni disponibles. Le problème est traité dans un contexte faiblement supervisé dans lequel nous proposons un framework d’apprentissage de représentation pour la classification robuste des événements audiovisuels, la localisation des objets visuels, la détection des événements audio et la séparation de sources.Nous avons testé de manière approfondie les idées proposées sur des ensembles de données publics. Ces expériences permettent de faire un lien avec des phénomènes intuitifs et multimodaux que les humains utilisent dans leur processus de compréhension de scènes audiovisuellesThe goal of this thesis is to design algorithms that enable robust detection of objectsand events in videos through joint audio-visual analysis. This is motivated by humans’remarkable ability to meaningfully integrate auditory and visual characteristics forperception in noisy scenarios. To this end, we identify two kinds of natural associationsbetween the modalities in recordings made using a single microphone and camera,namely motion-audio correlation and appearance-audio co-occurrence.For the former, we use audio source separation as the primary application andpropose two novel methods within the popular non-negative matrix factorizationframework. The central idea is to utilize the temporal correlation between audio andmotion for objects/actions where the sound-producing motion is visible. The firstproposed method focuses on soft coupling between audio and motion representationscapturing temporal variations, while the second is based on cross-modal regression.We segregate several challenging audio mixtures of string instruments into theirconstituent sources using these approaches.To identify and extract many commonly encountered objects, we leverageappearance–audio co-occurrence in large datasets. This complementary associationmechanism is particularly useful for objects where motion-based correlations are notvisible or available. The problem is dealt with in a weakly-supervised setting whereinwe design a representation learning framework for robust AV event classification,visual object localization, audio event detection and source separation.We extensively test the proposed ideas on publicly available datasets. The experimentsdemonstrate several intuitive multimodal phenomena that humans utilize on aregular basis for robust scene understanding
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Stålnacke, Johanna. "Rough beginnings : Executive function in adolescents and young adults after preterm birth and repeat antenatal corticosteroid treatment." Doctoral thesis, Stockholms universitet, Psykologiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-106798.
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This thesis investigates long-term cognitive outcome in two cohorts of adolescents and young adults exposed to stressors during the perinatal period: one group born preterm (<37 weeks of gestation and birth weight <1,500 g); one group exposed to two or more courses of antenatal corticosteroids (ACS), to stimulate lung maturation in the face of threatening preterm birth. In fetal life the brain undergoes dramatic growth, and a disruption to the early establishment of functional neural networks may interrupt development in ways that are difficult to predict. Executive function refers to a set of cognitive processes that are important for purposeful regulation of thought, emotion, and behavior, and even a subtle depreciation may influence overall functioning. Study I investigated the stability of executive function development after preterm birth. Executive functions were differentiated into working memory and cognitive flexibility. Both components were highly stable from preschool age to late adolescence. In Study II, we identified subgroups within the group of children born preterm with respect to cognitive profiles at 5½ and 18 years, and identified longitudinal streams. Outcome after preterm birth was diverse, and insufficiently predicted by perinatal and family factors. Individuals performing at low levels at 5½ years were unlikely to improve over time, while a group of individuals performing at or above norm at 5½ years had improved their performance relative to term-born peers by age 18. Studies I and II pointed to the need for developmental monitoring of those at risk, prior to formal schooling. Study III investigated long-term cognitive outcome after repeat ACS treatment. The study did not provide support for the concern that repeat ACS exposure will have an adverse impact on cognitive function later in life. In sum, exposure to perinatal stressors resulted in great variation in outcome. However, for many, their rough beginnings had not left a lasting mark.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 1: Submitted.
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Johnson, Edward P. "Applying Bayesian Ordinal Regression to ICAP Maladaptive Behavior Subscales." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2121.pdf.
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Giolo, Suely Ruiz. "Variáveis latentes em análise de sobrevivência e curvas de crescimento." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-09052003-143659/.
Full textAbstract:
Em um contexto de analise de dados de sobrevivência univariados ou multivariados, dados de tempos de falha caracterizam-se pela possibilidade de poderem ser censurados. Embora comum na pratica, a censura impede o uso de alguns procedimentos estatisticos covencionais o que vem motivando, em especial apos a publicacao do artigo de Cox (1972), o desenvolvimento de metodos estatisticos nessa area. Uma linha de estudo recente e a de que, em algumas situacoes, a variavel resposta esteja sendo inuenciada por variaveis latentes, variaveis estas que sao usadas, em um sentido estatistico, para descreverem efeitos geneticos ou ambientais compartilhados pelos indivduos ou, ainda, covariaveis nao consideradas no estudo. Nesse trabalho, enfase e dada aos modelos de sobrevivencia que consideram tempos de falha multivariados e variaveis latentes. Esses tempos aparecem quando, por exemplo, cada individuo em estudo esta sujeito a diversos eventos ou, quando existe um agrupamento natural ou artificial o qual induz dependencia entre os tempos dos individuos do mesmo grupo. Modelos com variaveis latentes em que tais tempos de falha ocorrem em intervalos de tempo, ou seja, em um contexto de censura intervalar sao especialmente considerados nesse trabalho. O modelo de fragilidade gama para dados de sobrevivencia com censura intervalar e proposto, nesse trabalho, como um criterio para a selecao de bovinos. Como uma alternativa para esta selecao, o modelo de curvas de crescimento com efeitos aleatorios e tambem considerado. Para a estimacao dos parametros envolvidos em ambos os modelos propostos, programas computacionais sao apresentados. Uma abordagem Bayesiana e considerada no processo de estimação sendo, o metodo de Markov chain Monte Carlo (MCMC) utilizado e as distribuicoes a posteriori obtidas, usando-se o amostrador de Gibbs. O modelo de fragilidade gama com censura intervalar e o de curvas de crescimento com efeitos aleatorios sao comparados por meio de um estudo de simulação. Para ilustrar ambos os modelos propostos, estudos com bovinos das racas Nelore e Canchim são utilizados.In a context of univariate or multivariate survival data analysis, failure times data are characterized by the possibility to be censored. Although common in practice, censoring precludes the use of some conventional statistical procedures and it has been motivating, specially after the publication of the Cox's paper (1972), the development of statistical methods in this area. A recent topic of study is concerned with some situations where the response variable is in uenced by latent variables which are used in a statistical sense to describe genetic or environmental efects shared by individuals or also covariates not considered in the study. In this work emphasis is given to survival models which consider multivariate failure times and latent variables. Such times occur when, for instance, each individual under study is exposed to several events or when there is a natural or artificial clustering that causes dependence among times of those individuals at the same cluster. Models with latent variables where such failure times lie in intervals of time, i.e. in an interval censored context are specially considered in this work. The gamma frailty interval censored survival model is proposed in this work as a selection criterion for cattle. As an alternative selection criterion the growth curves model with random efects is also considered. To estimate the involved parameters in both proposed models, computational programs are presented. A Bayesian approach is considered in the estimation process so that the Markov chain Monte Carlo (MCMC) method is used and the posterior distributions are obtained using Gibbs sampling. The gamma frailty interval-censored survival model and the growth curves model with random efects are compared using a simulation study. To illustrate both proposed models studies with Nelore and Canchim cattle are used.
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Byrne, Elizabeth Mary. "Working memory training and transcranial electrical brain stimulation." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277101.
Full textAbstract:
Working memory training improves performance on trained and untrained working memory tasks, but there is little consistent evidence that these gains benefit everyday tasks that rely on working memory. Evidence has shown that transcranial electrical stimulation (tES) may be an effective tool for enhancing cognitive training and promoting transfer. In the first study, participants completed Cogmed working memory training with either active or sham transcranial random noise stimulation (tRNS). Training was associated with substantial gains on the training activities and on transfer measures of working memory with common processing and storage demands to the training tasks. tRNS did not enhance gains on trained or untrained activities. The second study systematically investigated the boundary conditions to training transfer by testing whether gains following backward digit recall (BDR) training transferred within- and across-paradigm to untrained backward recall and n-back tasks with varying degrees of overlap with the training activity. A further aim was to test whether transcranial direct current stimulation (tDCS) enhanced training and transfer. Participants were allocated to one of three conditions: (i) BDR training with active tDCS, (ii) BDR training with sham tDCS, or (iii) visual search control training with sham tDCS. The results indicated that training transfer is constrained by paradigm, but not by stimuli domain or stimuli materials. There was no evidence that tDCS enhanced performance on the training or transfer tasks. The results of Study 1 and Study 2 provide no evidence that tES enhances the benefits of working memory training. The absence of transfer between backward recall training and n-back in Study 2 suggested the tasks might tap into distinct aspects of working memory. Consequently, the final study used a latent variable approach to explore the degree of overlap between different forms of backward recall and n-back tasks containing digits, letters, or spatial locations as stimuli. The best-fitting factor model included two distinct but related (r = .68) constructs corresponding to backward recall and n-back. Both categories of task were linked to a separate fluid reasoning construct, providing evidence that both are valid measures of higher-order complex cognition. Overall, the experiments in this thesis suggest that working memory tasks tap into separate processes and that training may be targeting and improving these distinct processes, explaining the absence of cross-paradigm transfer.
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Täckström, Oscar. "Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision." Doctoral thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-197610.
Full textAbstract:
Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language.
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Nallaya, Sasikala. "The impact of multimodal texts on the development of English language proficiency." Thesis, 2010. http://hdl.handle.net/2440/62385.
Full textAbstract:
This research study titled ‘The Impact of Multimodal Texts on the Development of English language proficiency’ is conceived from three problem statements: (a) Is the Communicative English One (CE1) course effective? (b) Is the use of multimodal technologies useful? and (c) Is it useful to have knowledge about students’ learning needs in the design of a course, as well as taking cognisance of their needs in the planning? The CE1 course guided this research study. A sample of 400 students was drawn out of a total of 1300. Students’ English language proficiency was assessed before they started the course, at the end of the course and three months after they had completed the course. An English Proficiency Test (EPT) was administered on three occasions to assess change in performance with respect to learning English. Information about students’ background characteristics, in addition to the processes involved in the course and those related to the course was collected. The English
Proficiency Test was used to assess the students’ performance on the course and the data were analysed with Partial Least Squares Path Analysis to examine the mediating effects that influenced outcomes. Hierarchical Linear Modelling was used to examine the moderating effects that influenced the outcomes. Thirteen students were also interviewed to obtain an indepth perspective of the situation. Students’ written responses to open-ended questions were also analysed.
The key findings are: (a) multimodal technologies are effective in English language learning, (b) there is a gain in performance of students who enrolled for the CE1 course with low English proficiency, (c) while the girls do not lose, the boys increase noticeably in performance, (d) students from the East Coast region do not progress as rapidly as other regions, (e) students in some faculties gain more than students in other faculties and (f) there are faculty differences in the benefits obtained from the course. The implication of this research study to the theory of language learning is that multimodal technology increases the informal learning of English as both a second and foreign language alongside formal instruction in the classroom. Thus the use of technology can supplement the learning of a second and a foreign language in ways similar to the learning that takes place within the community in second language learning. This research study indicates that there are important benefits from the application of multimodal technologies that can be used for foreign language learning as well as for broadening second language learning.Thesis (Ph.D.) -- University of Adelaide, School of Education, 2010
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Mohanlal, Pramod. "Structural equation modelling." Diss., 1997. http://hdl.handle.net/10500/17475.
Full textAbstract:
Over the past two decades there has been an upsurge in interest in structural equation
modelling (SEM). Applications abound in the social sciences and econometrics, but
the use of this multivariate technique is not so common in public health research.
This dissertation discusses the methodology, the criticisms and practical problems of
SEM. We examine actual applications of SEM in public health research.
Comparisons are made between multiple regression and SEM and between factor
analysis and SEM. A complex model investigating the utilization of antenatal care
services (ANC) by migrant women in Belgium is analysed using SEM. The
dissertation concludes with a discussion of the results found and on the use of SEM
in public health research. Structural equation modelling is recommended as a tool for
public health researchers with a warning against using the technique too casually.Mathematical Sciences
M. Sc. (Statistics)
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Liu, Yan. "Latent variable modeling for biomarker analysis." 2012. https://scholarworks.umass.edu/dissertations/AAI3518256.
Full textAbstract:
Characterizing associations among multiple single-nucleotide polymorphisms (SNPs) within and across genes, and measures of disease progression or disease status will potentially offer new insight into disease etiology and disease progression. However, several analytical challenges arise due to the existence of multiple potentially informative genetic loci, as well as environmental and demographic factors, and the generally uncharacterized and complex relationships among them. Latent variable modeling offers a natural framework for data arising from these population-based association studies to uncover simultaneous effects of multiple biomarkers. In the first chapter, we describe applications and performance of two such latent variable methods, namely structural equation models (SEMs) and mixed effects models (MEMs), and highlight their theoretical overlap. The relative advantages of each paradigm are investigated through simulation studies and an application to data arising from a study of anti-retroviral-associated dyslipidemia in HIV-1 infected individuals is provided for illustration. In the second chapter, we address a prediction-based classification (PBC) method that allows the use of repeatedly measured biomarkers for CD4 + T cell outcome prediction through first-stage of fitting MEMs and subsequent classification based on clinical relevant thresholds ( CD4+ T cell count 200 or 350 cells/mm 3). Then we apply this PBC approach to a prospective cohort of HIV-1 infected subjects (n=3357) monitored upon anti-retroviral therapy initiation in 7 clinical sites with distinct geographical and socio-economic settings.
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Sahani, Maneesh. "Latent variable models for neural data analysis." Thesis, 1999. https://thesis.library.caltech.edu/7598/2/Sahani%201999.pdf.
Full textAbstract:
The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.
It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.
The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.
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"Influence analysis of some complicated latent variable models." 2002. http://library.cuhk.edu.hk/record=b6073458.
Full textAbstract:
Xu Liang."June 2002."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2002.
Includes bibliographical references (p. 74-82).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
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Liu, Xiang. "Three Contributions to Latent Variable Modeling." Thesis, 2019. https://doi.org/10.7916/D8Q25H61.
Full textAbstract:
The dissertation includes three papers that address some theoretical and technical issues of latent variable models. The first paper extends the uniformly most powerful test approach for testing person parameter in IRT to the two-parameter logistic models. In addition, an efficient branch-and-bound algorithm for computing the exact p-value is proposed. The second paper proposes a reparameterization of the log-linear CDM model. A Gibbs sampler is developed for posterior computation. The third paper proposes an ordered latent class model with infinite classes using a stochastic process prior. Furthermore, a nonparametric IRT application is also discussed.