Готові списки джерел за темами / Latent variable path analysis

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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Latent variable path analysis"

1

Coffman, Donna L., and Robert C. MacCallum. "Using Parcels to Convert Path Analysis Models Into Latent Variable Models." Multivariate Behavioral Research 40, no. 2 (April 2005): 235–59. http://dx.doi.org/10.1207/s15327906mbr4002_4.

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2

Barrett, Paul. "Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis." Personality and Individual Differences 29, no. 5 (November 2000): 999–1000. http://dx.doi.org/10.1016/s0191-8869(99)00239-1.

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3

Wardrop, James L., and John C. Loehlin. "Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis." Journal of Educational Statistics 12, no. 4 (1987): 410. http://dx.doi.org/10.2307/1165058.

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4

Kline, Rex B. "Latent variable path analysis in clinical research: A beginner's tour guide." Journal of Clinical Psychology 47, no. 4 (July 1991): 471–84. http://dx.doi.org/10.1002/1097-4679(199107)47:4<471::aid-jclp2270470402>3.0.co;2-o.

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5

Boomsma, Anne. "Book Review : Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis." Applied Psychological Measurement 12, no. 2 (June 1988): 211–13. http://dx.doi.org/10.1177/014662168801200212.

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6

Devlieger, Ines, and Yves Rosseel. "Factor Score Path Analysis." Methodology 13, Supplement 1 (June 1, 2017): 31–38. http://dx.doi.org/10.1027/1614-2241/a000130.

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Abstract. Theoretical researchers consider Structural Equation Modeling (SEM) to be the preferred method to study the relationships among latent variables. However, SEM has the disadvantage of requiring a large sample size, especially if the model is complex. Furthermore, since SEM estimates all parameters simultaneously, one misspecification in the model may influence the whole model. For these reasons, applied researchers often use a two-step Factor Score Regression (FSR) approach. In the first step, factor scores are calculated for the latent variables, which are used to perform a linear regression in the second step. However, this method results in incorrect regression coefficients. Croon (2002) developed a method that corrects for this bias. We combine this method of Croon (2002) with path analysis, resulting in Factor Score Path Analysis. This method results in correct path coefficients and has some advantages over SEM: it requires smaller sample sizes, can handle more complex models and the method is less sensitive to misspecifications, because of its stepwise nature. In conclusion, this method can be a suitable alternative for SEM, when one is dealing with a complex model and small sample sizes.
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7

Febriandani, Hasna, Abdul Muhaimin, and Dwi Andriani. "The Analysis of Purchase Intention of Processed Apple Products in Shopee." HABITAT 32, no. 3 (December 1, 2021): 173–83. http://dx.doi.org/10.21776/ub.habitat.2021.032.3.19.

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During the current pandemic, almost everything we need, both primary and secondary, we can fulfill from online services. In the era of globalization and the ease of online shopping transactions can lead to consumptive behavior, especially considering the current situation in the corona virus crisis, where consumer behavior has begun to change drastically. This study used Structural Equation Modeling-Partial Least Square (SEM-PLS) analysis with the WarpPLS approach, to predict the magnitude of the relationship among latent variables and between latent variable and its indicator. The dominant variables in the research on the effect of purchase intention of processed apple products at Shopee are trust, online shopping attitudes, subjective norms and purchase intentions. Trust in product purchase intention produces a path coefficient of 0.320 with a confidence interval value [0.160;0.481]. The test results showed that the path coefficient was positive with a high significance level. Online shopping attitude on product purchase intention produces a path coefficient of 0.422 with a confidence interval value [0.261; 0.627], the path coefficient was positive with a high significance level so that this sub hypothesis can be accepted. Subjective norm on product purchase intention produces a path coefficient of 0.250 with a confidence interval value [0.104;0.397], the path coefficient was positive with a high significance level. Purchase intention on purchasing decisions produces a path coefficient of 0.509 with a confidence interval value [0.370; 0.648], the path coefficient was positive with a high significance level.
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8

Tanaka, Akihiro, and Kuninori Nakamura. "Auditory Memory and Proficiency of Second Language Speaking: A Latent Variable Analysis Approach." Psychological Reports 95, no. 3 (December 2004): 723–34. http://dx.doi.org/10.2466/pr0.95.3.723-734.

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Previous studies of second language aptitude have mainly used verbal stimuli in memory tasks. Memory for musical stimuli has not been used in aptitude studies although music and language have structural similarity. In this study, 30 Japanese university students who speak English as a second language (19 men, M = 21.3 yr., SD = 1.8) participated in the experiment as volunteers. They performed verbal memory tasks, musical memory tasks, and English pronunciation tasks. Factor analysis indicated that verbal and musical memory abilities are better represented as a unitary factor rather than two independent factors. Further, a path analysis supported the hypothesis that the memory for both verbal and musical tasks affects proficiency of second language pronunciation, including prosodic features such as stress in word or intonation through a couple of sentences. The memory factor was interpreted as reflecting the performance of “auditory working memory.”
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9

Eshima, Nobuoki, Chooichiro Asano, and Minoru Tabata. "A developmental path model and causal analysis of latent dichotomous variables." British Journal of Mathematical and Statistical Psychology 49, no. 1 (May 1996): 43–56. http://dx.doi.org/10.1111/j.2044-8317.1996.tb01074.x.

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10

McDONALD, RODERICK P. "The Bilevel Reticular Action Model for Path Analysis with Latent Variables." Sociological Methods & Research 22, no. 3 (February 1994): 399–413. http://dx.doi.org/10.1177/0049124194022003007.

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Дисертації з теми "Latent variable path analysis"

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.
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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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3

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.
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4

Burnham, Alison J. "Multivariate latent variable regression : modelling and estimation /." *McMaster only, 1997.

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5

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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6

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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8

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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9

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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10

Ridall, Peter Gareth. "Bayesian Latent Variable Models for Biostatistical Applications." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16164/.

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Анотація:
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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Книги з теми "Latent variable path analysis"

1

Heinen, Ton. Discrete latent variable models. Tilburg, Netherlands: Tilburg University Press, 1993.

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2

Lohmöller, Jan-Bernd. Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag, 1989.

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3

Loehlin, John C. Latent variable models: An introduction to factor, path, and structural analysis. 2nd ed. Hillsdale, N.J: Lawrence Erlbaum Associates, 1992.

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4

Loehlin, John C. Latent variable models: An introduction to factor, path, and structural analysis. Hillsdale, N.J: L. Erlbaum Associates, 1987.

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5

Latent variable models: An introduction to factor, path, and structural analysis. 3rd ed. Mahwah, N.J: Lawrence Erlbaum, 1998.

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6

Latent variable models: An introduction to factor, path, and structural equation analysis. 4th ed. Mahwah, N.J: L. Erlbaum Associates, 2004.

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7

A, Marcoulides George, and Moustaki Irini, eds. Latent variable and latent structure models. Mahwah, N.J: Lawrence Earlbaum Publishers, 2002.

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8

Vigneron, Vincent, Vicente Zarzoso, Eric Moreau, Rémi Gribonval, and Emmanuel Vincent, eds. Latent Variable Analysis and Signal Separation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4.

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9

Bartholomew, David, Martin Knott, and Irini Moustaki. Latent Variable Models and Factor Analysis. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119970583.

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10

Tichavský, Petr, Massoud Babaie-Zadeh, Olivier J. J. Michel, and Nadège Thirion-Moreau, eds. Latent Variable Analysis and Signal Separation. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0.

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Частини книг з теми "Latent variable path analysis"

1

Lohmöller, Jan-Bernd. "Latent Variables Three-Mode Path (LVP3) Analysis." In Latent Variable Path Modeling with Partial Least Squares, 227–40. Heidelberg: Physica-Verlag HD, 1989. http://dx.doi.org/10.1007/978-3-642-52512-4_6.

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2

Hoyle, Rick H. "Path analysis and structural equation modeling with latent variables." In APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological., 333–67. Washington: American Psychological Association, 2012. http://dx.doi.org/10.1037/13620-019.

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3

Fong, Daniel Yee Tak. "Latent Variable Path Models." In Encyclopedia of Quality of Life and Well-Being Research, 3520–23. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1606.

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Fong, Daniel Yee Tak. "Latent Variable Path Models." In Encyclopedia of Quality of Life and Well-Being Research, 1–4. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-69909-7_1606-2.

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5

Mat Roni, Saiyidi, and Hadrian Geri Djajadikerta. "Latent Variable." In Data Analysis with SPSS for Survey-based Research, 89–104. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0193-4_6.

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6

Geladi, Paul, Hans Grahn, and Kim H. Esbensen. "Latent Variable Regression for Laboratory Hyperspectral Images." In Partial Least Squares Path Modeling, 339–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64069-3_16.

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Westland, J. Christopher. "Frontiers in Latent Variable Analysis." In Structural Equation Models, 127–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12508-0_8.

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Ishteva, Mariya. "Tensors and Latent Variable Models." In Latent Variable Analysis and Signal Separation, 49–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_6.

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Yılmaz, Y. Kenan, and A. Taylan Cemgil. "Probabilistic Latent Tensor Factorization." In Latent Variable Analysis and Signal Separation, 346–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4_43.

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10

Bartholomew, David J. "Factor Analysis and Latent Variable Modelling." In International Encyclopedia of Statistical Science, 501–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_247.

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Тези доповідей конференцій з теми "Latent variable path analysis"

1

Tang, Liang, Allan J. Volponi, and Ethan Prihar. "Extending Engine Gas Path Analysis Using Full Flight Data." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90161.

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Abstract The ability to trend engine module performance and provide engine system fault detection and isolation are arguably, core capabilities for any engine Condition Based Maintenance (CBM) system. The origins of on-condition monitoring can be traced back nearly four decades, and a methodology known as Gas Path Analysis (GPA) has played a pivotal role in its evolution. Legacy Gas Path Analysis is a general methodology that assesses and quantifies changes in the underlying performance of the major modules of the engine (compressors and turbines), which in turn, directly affect overall performance measures of interest such as fuel consumption, power availability, compressor surge margins, and the like. Additionally, this approach is easily adapted to enable fault detection and identification of many engine system accessory faults such as variable stator vanes, handling and customer bleeds, sensor biases, and drift. Classical GPA has been confined to off-board analysis of averaged snapshot data when the engine is in steady state operation. This discrete data point approach, while reasonably accurate and repeatable, introduces a time latency to confidently detect and identify a faulty condition. Depending on the type and severity of the underlying fault, time to identify can be the differentiating factor in avoiding an unanticipated engine removal, flight delay or cancellation, in-flight engine shutdown, or catastrophic event. In this paper, we explore the use of streaming full flight data, which includes both transient and steady state operation. This type of data stream, when properly processed by gas path analysis and information fusion algorithms, allows faster anomaly detection, credible fault persistency checks, and timely fault identification within the current flight.
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Jiang, Xiubao, Xinge You, Yi Mou, Shujian Yu, and Wu Zeng. "Gaussian latent variable models for variable selection." In 2014 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2014. http://dx.doi.org/10.1109/spac.2014.6982714.

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Briones, Alejandro M., Nathan Thomas, and Brent A. Rankin. "Effects of Combustor Enclosure Flow Path on Combustor Design." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14127.

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Abstract A design optimization procedure was implemented to resize the holes of a combustor liner for practical applications. A combustor geometry evaluated without an enclosure was to be reformulated within an enclosure. The objective functions of the combustor with enclosure involved targeting the flow splits of the combustor without enclosure. Latin Hypercube Sampling (LHS) design of experiments (DOE) was utilized to obtain at least a pure quadratic response surface (RS). These were computed using Genetic Aggregate (GA). These RS were, in turn, evaluated by a multiple objective genetic algorithm (MOGA) optimizer. The focus of this study was a small-scale cavity-stabilized combustor. Steady, compressible three-dimensional simulations are performed using a multi-phase Realizable k-ε Reynolds-averaged Navier-Stokes (RANS) approach. Combustion-turbulence interaction is modeled with flamelet progress variable (FPV) and β-presumed probability density function (PDF). There are eleven input and output parameters corresponding to the combustor hole sizes and associated mass flow rates. The RS obtained with GA were principally of the Kriging kind (with constant and linear trends and damped sinusoid and Gaussian kernels). A combustor hole mass flow rate was mainly determined by its hole size but was also influenced by the other holes. The combustor flow split non-linearity shows that increasing a hole size increases its mass flow rate, but simultaneously decreases another hole flow rate. This was also verified by sensitivity analysis. Due to this non-linearity, matching flow splits between geometry without and with enclosure is challenging and may not be possible for some situations. Thus, it is concluded that optimization of the combustor geometry without the enclosure is not the best route. Rather, it would be better for the geometry to be optimized with the enclosure included in order to account for flow separation and non-linear influence of the combustor holes on the flow field.
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Ackermann, Etienne, Caleb Kemere, Kourosh Maboudi, and Kamran Diba. "Latent variable models for hippocampal sequence analysis." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335439.

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Zhu, Chen, Hengshu Zhu, Hui Xiong, Pengliang Ding, and Fang Xie. "Recruitment Market Trend Analysis with Sequential Latent Variable Models." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939689.

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Molgaard, Lasse L., Jan Larsen, and Cyril Goutte. "Temporal analysis of text data using latent variable models." In 2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2009. http://dx.doi.org/10.1109/mlsp.2009.5306265.

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Luengo, David, and Victor Elvira. "Latent Variable Analysis of Causal Interactions in Atrial Fibrillation Electrograms." In 2016 Computing in Cardiology Conference. Computing in Cardiology, 2016. http://dx.doi.org/10.22489/cinc.2016.284-471.

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Xiong, Hao, Shlomo Berkovsky, Roneel V. Sharan, Sidong Liu, and Enrico Coiera. "Robust Vision-Based Workout Analysis Using Diversified Deep Latent Variable Model." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175454.

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Qu, Ying, Jian Jia, and Qi-zong Wu. "Latent variable study algorithm based on grey cluster relation analysis method." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5192732.

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Pei Ling Lai and C. Fyfe. "A Latent Variable Implementation of Canonical Correlation Analysis for Data Visualisation." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246819.

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