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Academic literature on the topic 'Latent Covariates'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Latent Covariates"

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Mäkikangas, Anne, Asko Tolvanen, Kaisa Aunola, Taru Feldt, Saija Mauno, and Ulla Kinnunen. "Multilevel Latent Profile Analysis With Covariates." Organizational Research Methods 21, no. 4 (February 22, 2018): 931–54. http://dx.doi.org/10.1177/1094428118760690.

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Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of the data affect the probability of a membership of a particular profile. We demonstrate the use of MLPA by investigating job characteristics profiles based on the job-demand-control-support (JDCS) model using data from 1,958 university employees clustered in 78 work departments. The implications of the results for organizational research are discussed, together with several issues related to the potential of MLPA for wider application.
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Poon, Wai-Yin, and Hai-Bin Wang. "Latent variable models with ordinal categorical covariates." Statistics and Computing 22, no. 5 (October 12, 2011): 1135–54. http://dx.doi.org/10.1007/s11222-011-9290-8.

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Lagishetty, Chakradhar V., Carolyn V. Coulter, and Stephen B. Duffull. "Design of pharmacokinetic studies for latent covariates." Journal of Pharmacokinetics and Pharmacodynamics 39, no. 1 (December 10, 2011): 87–97. http://dx.doi.org/10.1007/s10928-011-9231-3.

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Schofield, Lynne Steuerle, Brian Junker, Lowell J. Taylor, and Dan A. Black. "Predictive Inference Using Latent Variables with Covariates." Psychometrika 80, no. 3 (September 18, 2014): 727–47. http://dx.doi.org/10.1007/s11336-014-9415-z.

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Ferrari, Diogo. "Modeling Context-Dependent Latent Effect Heterogeneity." Political Analysis 28, no. 1 (May 20, 2019): 20–46. http://dx.doi.org/10.1017/pan.2019.13.

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Classical generalized linear models assume that marginal effects are homogeneous in the population given the observed covariates. Researchers can never be sure a priori if that assumption is adequate. Recent literature in statistics and political science have proposed models that use Dirichlet process priors to deal with the possibility of latent heterogeneity in the covariate effects. In this paper, we extend and generalize those approaches and propose a hierarchical Dirichlet process of generalized linear models in which the latent heterogeneity can depend on context-level features. Such a model is important in comparative analyses when the data comes from different countries and the latent heterogeneity can be a function of country-level features. We provide a Gibbs sampler for the general model, a special Gibbs sampler for gaussian outcome variables, and a Hamiltonian Monte Carlo within Gibbs to handle discrete outcome variables. We demonstrate the importance of accounting for latent heterogeneity with a Monte Carlo exercise and with two applications that replicate recent scholarly work. We show how Simpson’s paradox can emerge in the empirical analysis if latent heterogeneity is ignored and how the proposed model can be used to estimate heterogeneity in the effect of covariates.
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Li, Ming, and Jeffrey R. Harring. "Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study." Educational and Psychological Measurement 77, no. 5 (June 15, 2016): 766–91. http://dx.doi.org/10.1177/0013164416653789.

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Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more meaningful. Very few studies have been conducted that compare the performance of various approaches to estimating covariate effects in mixture modeling, and fewer yet have considered more complicated models such as growth mixture models where the latent class variable is more difficult to identify. A Monte Carlo simulation was conducted to investigate the performance of four estimation approaches: (1) the conventional three-step approach, (2) the one-step maximum likelihood (ML) approach, (3) the pseudo class (PC) approach, and (4) the three-step ML approach in terms of their ability to recover covariate effects in the logistic regression class membership model within a growth mixture modeling framework. Results showed that when class separation was large, the one-step ML approach and the three-step ML approach displayed much less biased covariate effect estimates than either the conventional three-step approach or the PC approach. When class separation was poor, estimation of the relation between the dichotomous covariate and latent class variable was severely affected when the new three-step ML approach was used.
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Nguyen, Trang Quynh, and Elizabeth A. Stuart. "Propensity Score Analysis With Latent Covariates: Measurement Error Bias Correction Using the Covariate’s Posterior Mean, aka the Inclusive Factor Score." Journal of Educational and Behavioral Statistics 45, no. 5 (April 8, 2020): 598–636. http://dx.doi.org/10.3102/1076998620911920.

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We address measurement error bias in propensity score (PS) analysis due to covariates that are latent variables. In the setting where latent covariate X is measured via multiple error-prone items W, PS analysis using several proxies for X—the W items themselves, a summary score (mean/sum of the items), or the conventional factor score (i.e., predicted value of X based on the measurement model)—often results in biased estimation of the causal effect because balancing the proxy (between exposure conditions) does not balance X. We propose an improved proxy: the conditional mean of X given the combination of W, the observed covariates Z, and exposure A, denoted [Formula: see text]. The theoretical support is that balancing [Formula: see text] (e.g., via weighting or matching) implies balancing the mean of X. For a latent X, we estimate [Formula: see text] by the inclusive factor score (iFS)—predicted value of X from a structural equation model that captures the joint distribution of [Formula: see text] given Z. Simulation shows that PS analysis using the iFS substantially improves balance on the first five moments of X and reduces bias in the estimated causal effect. Hence, within the proxy variables approach, we recommend this proxy over existing ones. We connect this proxy method to known results about valid weighting/matching functions. We illustrate the method in handling latent covariates when estimating the effect of out-of-school suspension on risk of later police arrests using National Longitudinal Study of Adolescent to Adult Health data.
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Zhang, Ningshan, and Jeffrey S. Simonoff. "Joint latent class trees: A tree-based approach to modeling time-to-event and longitudinal data." Statistical Methods in Medical Research 31, no. 4 (February 18, 2022): 719–52. http://dx.doi.org/10.1177/09622802211055857.

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In this paper, we propose a semiparametric, tree-based joint latent class model for the joint behavior of longitudinal and time-to-event data. Existing joint latent class approaches are parametric and can suffer from high computational cost. The most common parametric approach, the joint latent class model, further restricts analysis to using time-invariant covariates in modeling survival risks and latent class memberships. The proposed tree method (joint latent class tree) is fast to fit, and permits time-varying covariates in all of its modeling components. We demonstrate the prognostic value of using time-varying covariates, and therefore the advantage of joint latent class tree over joint latent class model on simulated data. We apply joint latent class tree to a well-known data set (the PAQUID data set) and confirm its superior prediction performance and orders-of-magnitude speedup over joint latent class model.
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Alhadabi, Amal. "Latent Heterogeneity in High School Academic Growth: A Comparison of the Performance of Growth Mixture Model, Structural Equation Modeling Tree, and Forest." Journal of Educational and Psychological Studies [JEPS] 16, no. 4 (November 30, 2022): 355–472. http://dx.doi.org/10.53543/jeps.vol16iss4pp355-472.

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The Growth Mixture Model (GMM) is associated with several class enumeration issues. The contemporary advancement of automated algorithms presents two promising alternatives that merge confirmatory Structural Equation Modeling (SEM) with exploratory data-mining algorithms: SEM Tree and SEM Forest. This study investigated the performance of the aforementioned three methods (i.e., the GMM, SEM Tree, and SEM Forest) to detect latent heterogeneity in academic growth across four high school grades using an illustrative subsample of the Longitudinal Study of High School of 2009. The findings showed remarkable differences in detecting latent heterogeneity across the three methods as indicated by a parsimonious number of classes, with more unique growth trajectories, capturing the latent heterogeneity in the growth factors. In contrast, SEM Tree and SEM Forest were better at tracking the influences of covariates in the model parameters’ heterogeneity, as indicated by providing more accurate measures of covariate importance and a detailed description of the role of covariates at each level of the tree or the forest. These findings imply the complementary use of these methods to obtain a clear separation between growth trajectories, as estimated by GMM; and the inclusion of most influential covariates, as identified by SEM Tree and Forest (208 words).
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Alhadabi, Amal. "Latent Heterogeneity in High School Academic Growth: A Comparison of the Performance of Growth Mixture Model, Structural Equation Modeling Tree, and Forest." Journal of Educational and Psychological Studies [JEPS] 16, no. 4 (December 4, 2022): 355–72. http://dx.doi.org/10.53543/jeps.vol16iss4pp355-372.

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The Growth Mixture Model (GMM) is associated with several class enumeration issues. The contemporary advancement of automated algorithms presents two promising alternatives that merge confirmatory Structural Equation Modeling (SEM) with exploratory data-mining algorithms: SEM Tree and SEM Forest. This study investigated the performance of the aforementioned three methods (i.e., the GMM, SEM Tree, and SEM Forest) to detect latent heterogeneity in academic growth across four high school grades using an illustrative subsample of the Longitudinal Study of High School of 2009. The findings showed remarkable differences in detecting latent heterogeneity across the three methods as indicated by a parsimonious number of classes, with more unique growth trajectories, capturing the latent heterogeneity in the growth factors. In contrast, SEM Tree and SEM Forest were better at tracking the influences of covariates in the model parameters’ heterogeneity, as indicated by providing more accurate measures of covariate importance and a detailed description of the role of covariates at each level of the tree or the forest. These findings imply the complementary use of these methods to obtain a clear separation between growth trajectories, as estimated by GMM; and the inclusion of most influential covariates, as identified by SEM Tree and Forest (208 words).
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Dissertations / Theses on the topic "Latent Covariates"

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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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Rockwood, Nicholas John. "Estimating Multilevel Structural Equation Models with Random Slopes for Latent Covariates." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478681581538.

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Wang, Junhua. "Large-Sample Logistic Regression with Latent Covariates in a Bayesian Networking Context." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/103.

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We considered the problem of predicting student retention using logistic regression when the most important covariates such as the college variables are latent, but the network structure is known. This network structure specifies the relationship between pre-college to college variables and then from college to student retention variables. Based on this structure, we developed three estimators, examined their large-sample properties, and evaluated their empirical efficiencies using WKU student retention database. Results show that while the hat estimator is expected to be most efficient, the tilde estimator was shown to be more efficient than the check method. This increased efficiency suggests that utilizing the network information can improve our predictions.
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Wang, Yan. "Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7715.

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Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This Monte Carlo simulation study examined the issue of measurement invariance testing with FMM when there are covariate effects. Specifically, this study investigated the impact of excluding and misspecifying covariate effects on the class enumeration and measurement invariance testing with FMM. Data were generated based on three FMM models where the covariate had impact on the latent class membership only (population model 1), both the latent class membership and the factor (population model 2), and the latent class membership, the factor, and one item (population model 3). The number of latent classes was fixed at two. These two latent classes were distinguished by factor mean difference for conditions where measurement invariance held in the population, and by both factor mean difference and intercept differences across classes when measurement invariance did not hold in the population. For each of the population models, different analysis models that excluded or misspecified covariate effects were fitted to data. Analyses consisted of two parts. First, for each analysis model, class enumeration rates were examined by comparing the fit of seven solutions: 1-class, 2-class configural, metric, and scalar, and 3-class configural, metric, and scalar. Second, assuming the correct solution was selected, the fit of analysis models with the same solution was compared to identify a best-fitting model. Results showed that completely excluding the covariate from the model (i.e., the unconditional model) would lead to under-extraction of latent classes, except when the class separation was large. Therefore, it is recommended to include covariate in FMM when the focus is to identify the number of latent classes and the level of invariance. Specifically, the covariate effect on the latent class membership can be included if there is no priori hypothesis regarding whether measurement invariance might hold or not. Then fit of this model can be compared with other models that included covariate effects in different ways but with the same number of latent classes and the same level of invariance to identify a best-fitting model.
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Harman, David M. "Stochastic process customer lifetime value models with time-varying covariates." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2221.

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Customer lifetime value (CLV) is a forecasted expectation of the future value of a customer to the firm. There are two customer behavioral components of CLV that represent a particular modeling challenge: 1) how many transactions we expect from a customer in the future, and 2) how likely it is the customer remains active. Existing CLV models like the Pareto/NBD are valuable managerial tools because they are able to provide forward-looking estimates of transaction patterns and customer churn when the event of a customer leaving is unobservable, which is typical for most noncontractual goods and services. The CLV model literature has for the most part maintained its original assumption that the number of customer transactions follows a stable transaction process. Yet there are many categories of noncontractual goods and services where the stable transaction rate assumption is violated, particularly seasonal purchase patterns. CLV model estimates are further biased when there is an excess of customers with no repeat transactions. To address these modeling challenges, within this thesis I develop a generalized CLV modeling framework that combines three elements necessary to reduce bias in model estimates: 1) the incorporation of time-varying covariates to model data with transaction rates that change over time, 2) a zero-inflated model specification for customers with no repeat transactions, and 3) generalizes to different transaction process distributions to better fit diverse customer transaction patterns. This CLV modeling framework provides firms better estimates of the future activity of their customers, a critical CRM application.
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Flory, Felix [Verfasser], Rolf [Gutachter] Steyer, Michael [Gutachter] Eid, and Andreas [Gutachter] Klein. "Average treatment effects in regression models with interactions between treatment and manifest or latent covariates / Felix Flory ; Gutachter: Rolf Steyer, Michael Eid, Andreas Klein." Jena : Friedrich-Schiller-Universität Jena, 2008. http://d-nb.info/1178544117/34.

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Hatzinger, Reinhold, and Walter Katzenbeisser. "Log-linear Rasch-type models for repeated categorical data with a psychobiological application." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/126/1/document.pdf.

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The purpose of this paper is to generalize regression models for repeated categorical data based on maximizing a conditional likelihood. Some existing methods, such as those proposed by Duncan (1985), Fischer (1989), and Agresti (1993, and 1997) are special cases of this latent variable approach, used to account for dependencies in clustered observations. The generalization concerns the incorporation of rather general data structures such as subject-specific time-dependent covariates, a variable number of observations per subject and time periods of arbitrary length in order to evaluate treatment effects on a categorical response variable via a linear parameterization. The response may be polytomous, ordinal or dichotomous. The main tool is the log-linear representation of appropriately parameterized Rasch-type models, which can be fitted using standard software, e.g., R. The proposed method is applied to data from a psychiatric study on the evaluation of psychobiological variables in the therapy of depression. The effects of plasma levels of the antidepressant drug Clomipramine and neuroendocrinological variables on the presence or absence of anxiety symptoms in 45 female patients are analyzed. The individual measurements of the time dependent variables were recorded on 2 to 11 occasions. The findings show that certain combinations of the variables investigated are favorable for the treatment outcome. (author´s abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Jay, Flora. "Méthodes bayésiennes en génétique des populations : relations entre structure génétique des populations et environnement." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS026/document.

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Nous présentons une nouvelle méthode pour étudier les relations entre la structure génétique des populations et l'environnement. Cette méthode repose sur des modèles hiérarchiques bayésiens qui utilisent conjointement des données génétiques multi-locus et des données spatiales, environnementales et/ou culturelles. Elle permet d'estimer la structure génétique des populations, d'évaluer ses liens avec des covariables non génétiques, et de projeter la structure génétique des populations en fonction de ces covariables. Dans un premier temps, nous avons appliqué notre approche à des données de génétique humaine pour évaluer le rôle de la géographie et des langages dans la structure génétique des populations amérindiennes. Dans un deuxième temps, nous avons étudié la structure génétique des populations pour 20 espèces de plantes alpines et nous avons projeté les modifications intra spécifiques qui pourront être causées par le réchauffement climatique
We introduce a new method to study the relationships between population genetic structure and environment. This method is based on Bayesian hierarchical models which use both multi-loci genetic data, and spatial, environmental, and/or cultural data. Our method provides the inference of population genetic structure, the evaluation of the relationships between the structure and non-genetic covariates, and the prediction of population genetic structure based on these covariates. We present two applications of our Bayesian method. First, we used human genetic data to evaluate the role of geography and languages in shaping Native American population structure. Second, we studied the population genetic structure of 20 Alpine plant species and we forecasted intra-specific changes in response to global warming. STAR
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Crespo, Cuaresma Jesus, Bettina Grün, Paul Hofmarcher, Stefan Humer, and Mathias Moser. "Unveiling Covariate Inclusion Structures In Economic Growth Regressions Using Latent Class Analysis." Elsevier, 2016. http://dx.doi.org/10.1016/j.euroecorev.2015.03.009.

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We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.
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Pereira, Gilberto de Araujo. "Avaliação de testes diagnósticos na ausência de padrão ouro considerando relaxamento da suposição de independência condicional, covariáveis e estratificação da população: uma abordagem Bayesiana." Universidade Federal de São Carlos, 2011. https://repositorio.ufscar.br/handle/ufscar/4486.

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The application of a gold standard reference test in all or part of the sample under investigation is often not feasible for the majority of diseases affecting humans, either by a lack of consensus on which testing may be considered a gold standard, the high level of invasion of the gold standard technique, the high cost of financially large-scale application, or by ethical questions, so to know the performance of existing tests is essential for the process of diagnosis of these diseases. In statistical modeling aimed to obtain robust estimates of the prevalence of the disease (x ) and the performance parameters of diagnostic tests (sensitivity (Se) and specificity (Sp)), various strategies have been considered such as the stratification of the population, the relaxation of the assumption of conditional independence, the inclusion of covariates, the verification type (partial or total) and the techniques to replace the gold standard. In this thesis we propose a new structure of stratification of the population considering both the prevalence rates and the parameters of test performance among the different strata (EHW). A Bayesian latent class modeling to estimate these parameters was developed for the general case of K diagnostic tests under investigation, relaxation of the assumption of conditional independence according to the formulations of the fixed effect (FECD) and random (RECD) with dependent order (h _ k) and M covariates. The application of models to two data sets about the performance evaluation of diagnostic tests used in screening for Chagas disease in blood donors showed results consistent with the sensitivity studies. Overall, we observed for the structure of stratification proposal (EHW) superior performance and estimates closer to the nominal values when compared to the structure of stratification when only the prevalence rates are different between the strata (HW), even when we consider data set with rates of Se, Sp and x close among the strata. Generally, the structure of latent class, when we have low or high prevalence of the disease, estimates of sensitivity and specificity rates have higher standard errors. However, in these cases, when there is high concordance of positive or negative results of the tests, the error pattern of these estimates are reduced. Regardless of the structure of stratification (EHW, HW), sample size and the different scenarios used to model the prior information, the model of conditional dependency from the FECD and RECD had, from the information criteria (AIC, BIC and DIC), superior performance to the structure of conditional independence (CI) and to FECD with improved performance and estimates closer to the nominal values. Besides the connection logit, derived from the logistic distribution with symmetrical shape, find in the link GEV, derived from the generalized extreme value distribution which accommodates symmetric and asymmetric shapes, a interesting alternative to construct the conditional dependence structure from the RECD. As an alternative to the problem of identifiability, present in this type of model, the criteria adopted to elicit the informative priors by combining descriptive analysis of data, adjustment models from simpler structures, were able to produce estimates with low standard error and very close to the nominal values.
Na área da saúde a aplicação de teste de referência padrão ouro na totalidade ou parte da amostra sob investigação é, muitas vezes, impraticável devido à inexistência de consenso sobre o teste a ser considerado padrão ouro, ao elevado nível de invasão da técnica, ao alto custo da aplicação em grande escala ou por questões éticas. Contudo, conhecer o desempenho dos testes é fundamental no processo de diagnóstico. Na modelagem estatística voltada à estimação da taxa de prevalência da doença (x ) e dos parâmetros de desempenho de testes diagnósticos (sensibilidade (S) e especificidade (E)), a literatura tem explorado: estratificação da população, relaxamento da suposição de independência condicional, inclusão de covariáveis, tipo de verificação pelo teste padrão ouro e técnicas para substituir o teste padrão ouro inexistente ou inviável de ser aplicado em toda a amostra. Neste trabalho, propomos uma nova estrutura de estratificação da população considerando taxas de prevalências e parâmetros de desempenho diferentes entre os estratos (HWE). Apresentamos uma modelagem bayesiana de classe latente para o caso geral de K testes diagnósticos sob investigação, relaxamento da suposição de independência condicional segundo as formulações de efeito fixo (DCEF) e efeito aleatório (DCEA) com dependência de ordem (h _ K) e inclusão de M covariáveis. A aplicação dos modelos a dois conjuntos de dados sobre avaliação do desempenho de testes diagnósticos utilizados na triagem da doença de Chagas em doadores de sangue apresentou resultados coerentes com os estudos de sensibilidade. Observamos, para a estrutura de estratificação proposta, HWE, desempenho superior e estimativas muito próximas dos valores nominais quando comparados à estrutura de estratificação na qual somente as taxas de prevalências são diferentes entre os estratos (HW), mesmo quando consideramos dados com taxas de S, E e x muito próximas entre os estratos. Geralmente, na estrutura de classe latente, quando temos baixa ou alta prevalência da doença, as estimativas das sensibilidades e especificidades apresentam, respectivamente, erro padrão mais elevado. No entanto, quando há alta concordância de resultados positivos ou negativos, tal erro diminui. Independentemente da estrutura de estratificação (HWE, HW), do tamanho amostral e dos diferentes cenários utilizados para modelar o conhecimento a priori, os modelos de DCEF e de DCEA apresentaram, a partir dos critérios de informação (AIC, BIC e DIC), desempenhos superiores à estrutura de independência condicional (IC), sendo o de DCEF com melhor desempenho e estimativas mais próximas dos valores nominais. Além da ligação logito, derivada da distribuição logística com forma simétrica, encontramos na ligação VEG , derivada da distribuição de valor extremo generalizada a qual acomoda formas simétricas e assimétricas, interessante alternativa para construir a estrutura de DCEA. Como alternativa ao problema de identificabilidade, neste tipo de modelo, os critérios para elicitar as prioris informativas, combinando análise descritiva dos dados com ajuste de modelos de estruturas mais simples, contribuíram para produzir estimativas com baixo erro padrão e muito próximas dos valores nominais.
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Books on the topic "Latent Covariates"

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Brian, Everitt, and Pickles Andrew, eds. Modelling covariances and latent variables using EQS. London: Chapman & Hall, 1993.

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Dunn, G. Modelling Covariances and Latent Variables Using Eqs. Taylor & Francis Group, 2020.

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Book chapters on the topic "Latent Covariates"

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Marshall, Adele H., Hannah Mitchell, and Mariangela Zenga. "Modelling the Length of Stay of Geriatric Patients in Emilia Romagna Hospitals Using Coxian Phase-Type Distributions with Covariates." In Advances in Latent Variables, 127–39. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/10104_2014_21.

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Galluccio, Carla, Rosa Fabbricatore, and Daniela Caso. "Exploring the intention to walk: a study on undergraduate students using item response theory and theory of planned behaviour." In Proceedings e report, 153–58. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.30.

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Physical activity is one of the most basic human functions, and it is an important foundation of health throughout life. Physical activity apports benefit on both physical and mental health, reducing the risk of several diseases and lowering stress reactions, anxiety and depression. More specifically, physical activity is defined as "any bodily movement produced by skeletal muscles that require energy expenditure" (World Health Organization), including in this definition several activities. Among them, walking has been shown to improve physical and mental well-being in every age group. Despite that, insufficient walking among university students has been increasingly reported, requiring walking promotion intervention. In order to do this, dividing students based on their intention to walk might be useful since the intention is considered as the best predictor of behaviour. In this work, we carried out a study on university students' intention to walk and some of its predictors by exploiting Item Response Theory (IRT) models. In particular, we inspected the predictors of intention by mean of Rating Scale Graded Response Model (RS-GRM). Then we used the Latent Class IRT model to divide students according to their intention to walk, including predictors' scores as covariates. We chose the intention's predictors according to an extension of the Theory of Planned Behaviour (TPB), with both classic and additional variables. The formers are attitude toward behaviour, subjective norms, and perceived behavioural control, whereas we used risk perception, self-efficacy, anticipation, self-identity and anticipated regret as additional variables. Data was collected administrating a self-report questionnaire to undergraduate students enrolled in the Psychology course at Federico II University of Naples.
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Sarra, Annalina, Adelia Evangelista, and Tonio Di Battista. "Assessment of visitors’ perceptions in protected areas through a model-based clustering." In Proceedings e report, 245–50. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.46.

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Protected areas are well-defined geographical spaces that, in view of their recognized, natural, ecological or cultural values, receive protection. They have the twofold mandate of protection of natural resources and providing a space for nature-based tourism activities. In the last years, the nature-based tourism is experiencing positive and sustainable growth worldwide. Understanding the value attached by visitors to their destination and know their assessment on various activities in which they are engaged during their stay is a key element in shaping tourist’s satisfaction. Objective of this research was to identify the profiles of visitors to tourist destinations within Natural Park of Majella (Abruzzo region, Italy) and to assess the link with their satisfaction. The data for this study were collected by means of a structured questionnaire administrated to tourists who visited the sites of the protected area during the last three summer months. A total of 150 valid questionnaires were obtained and form the base of the data analysis. Through a Bayesian model-based clustering, better known as Bayesian Profile Regression, we partition visitors into clusters, characterized by similar profiles in terms of their demographic characteristics (age, gender, education attainment), as well as, in terms of the features of their travel behaviour (accommodation, length of stay, past visitation experience). A further benefit of the followed approach lies in the ability of that Bayesian technique of simultaneously estimating the contribute of all covariates to the outcome of interest. In our context, we explore the association of detected groups with the tourists’ satisfaction. In the survey, the global quality of tourism service is segmented into single features and respondents were asked to give their level of appreciation on a five-point Likert satisfaction scale. To estimate the latent trait measured by the items and related to the overall satisfaction we followed an IRT modelling.
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Dayton, C. Mitchell, and George B. Macready. "A Latent Class Covariate Model with Applications to Criterion-Referenced Testing." In Latent Trait and Latent Class Models, 129–43. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4757-5644-9_7.

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Marasco, Emanuela, Mengling He, Larry Tang, and Sumanth Sriram. "Accounting for Demographic Differentials in Forensic Error Rate Assessment of Latent Prints via Covariate-Specific ROC Regression." In Communications in Computer and Information Science, 338–50. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1086-8_30.

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Schneeweiss, Hans, Chi-Lun Cheng, and Roland Wolf. "On the Bias of Structural Estimation Methods in a Polynomial Regression with Measurement Error When the Distribution of the Latent Covariate is Misspecified." In Contributions to Modern Econometrics, 209–22. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3602-1_14.

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Xiang, Brian, and Abdelrahman Abdelmonsef. "Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift." In HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction, 617–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17615-9_44.

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"Multilevel Models With Latent Variables and Covariates." In An Introduction to Multilevel Modeling Techniques, 204–38. Routledge, 2015. http://dx.doi.org/10.4324/9781315746494-14.

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Dayton, C. Mitchell, and George B. Macready. "Use of Categorical and Continuous Covariates in Latent Class Analysis." In Applied Latent Class Analysis, 213–33. Cambridge University Press, 2002. http://dx.doi.org/10.1017/cbo9780511499531.009.

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"- Including individual covariates and relaxing basic model assumptions." In Latent Markov Models for Longitudinal Data, 130–59. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b13246-9.

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Conference papers on the topic "Latent Covariates"

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Chung, Tammy, Marc Steinberg, Mary Bridgeman, and YingYing Chen. "Driving Under the Influence of Cannabis: Associations with Latent Profiles of Substance Use and Executive Cognitive Functioning." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.53.

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Background: Driving under the influence of cannabis (DUIC) almost doubles car crash risk (odds ratios range: 1.28-2.49). Known DUIC correlates include male gender, low perceived danger of DUIC, and greater frequency of cannabis and other drug use. Less is known about the role of executive cognitive functioning (e.g., skills in planning, organization) as a correlate of DUIC. Deficits in executive cognitive functioning could precede, and be exacerbated by heavy cannabis use, potentially contributing to DUIC risk. Objectives: This cross-sectional survey study used a person-centered analysis (latent profile analysis) to (1) identify prototypical profiles representing aspects of executive functioning and substance use in young adults, and (2) determine which profiles were associated with self-report of DUIC. We hypothesized that at least two profiles would be identified: mainly or only cannabis use vs polysubstance use. We also predicted that the polysubstance use profile would be associated with worse executive functioning and self-report of DUIC. Method: Young adults (N=69; ages 18-25; mean age=20.0 [SD=1.9]; 62.3% female; 75.4% White, 13.0% Black, 11.6% Other race/ethnicity) who reported weekly cannabis use were recruited from the community in Pittsburgh, PA to participate in a study of cannabis effects on cognition. Baseline collected demographics, self-reported age of cannabis use onset (age <16 vs age >16), NIDA modified ASSIST, Marijuana Withdrawal Checklist, Alcohol Use Disorders Identification Test (AUDIT), Behavior Rating Inventory of Executive Functioning (BRIEF) (working memory, organization/planning scales), and Marijuana Consequences Questionnaire (item on “driven a car when high” in past 6 months). Latent profile analysis (LatentGold 5.1) was used to identify distinct classes, testing the fit of 1-5 classes. Each model included 10 indicators: age of cannabis use onset, frequency of cannabis and tobacco use, cannabis withdrawal severity, ASSIST scores for cannabis, cocaine and hallucinogens (the substances most often reported), AUDIT score, and BRIEF working memory, and organization/planning scores. For the best fitting model, covariates (i.e., self-report of DUIC, age, gender) were examined as profile correlates in a separate, final step. Results: A model with 3 latent profiles was selected (see Figure). The profiles represented “Polysubstance Use” (40.8%), “Primary Cannabis” (22.3%), and “Later Onset Cannabis” (36.9%). Polysubstance use profile reported more cannabis-related problems and other drug use, and more problems with executive functioning than the other profiles (p<.05). Later Onset (vs Polysubstance Use) profile had older onset age (p<.05), and had the lowest level of cannabis involvement. Primary Cannabis and Later Onset profiles did not differ in report of problems with executive functioning. DUIC in the past 6 months (reported by 50.7% of the total sample) was more likely to be reported by Polysubstance use than Later Onset profile (p<.01). Polysubstance use profile was younger than Primary Cannabis profile (p<.05). The profiles did not differ by gender. Conclusions: As hypothesized, Polysubstance Use profile (which reported early cannabis use onset; and worse executive functioning, including problems with memory, planning/ organization) was associated with self-report of DUIC. Results highlight the role of self-reported executive functioning difficulties in DUIC risk, and the importance of targeting polysubstance use in preventing DUIC.
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Leno da Silva, Felipe, Raphael Cobe, and Renato Vicente. "A Tree-Adaptation Mechanism for Covariate and Concept Drift." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2022. http://dx.doi.org/10.52591/2021072414.

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Thanoon, Thanoon Y., and Robiah Adnan. "Improve the Bayesian generalized latent variable models with non-linear variable and covariate of dichotomous data." In SECOND INTERNATIONAL CONFERENCE OF MATHEMATICS (SICME2019). Author(s), 2019. http://dx.doi.org/10.1063/1.5097806.

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Antonio Delgado-Guerrero, Juan, Adria Colome, and Carme Torras. "Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9340709.

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Liu, Ziquan, Lei Yu, Janet H. Hsiao, and Antoni B. Chan. "Parametric Manifold Learning of Gaussian Mixture Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/426.

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The Gaussian Mixture Model (GMM) is among the most widely used parametric probability distributions for representing data. However, it is complicated to analyze the relationship among GMMs since they lie on a high-dimensional manifold. Previous works either perform clustering of GMMs, which learns a limited discrete latent representation, or kernel-based embedding of GMMs, which is not interpretable due to difficulty in computing the inverse mapping. In this paper, we propose Parametric Manifold Learning of GMMs (PML-GMM), which learns a parametric mapping from a low-dimensional latent space to a high-dimensional GMM manifold. Similar to PCA, the proposed mapping is parameterized by the principal axes for the component weights, means, and covariances, which are optimized to minimize the reconstruction loss measured using Kullback-Leibler divergence (KLD). As the KLD between two GMMs is intractable, we approximate the objective function by a variational upper bound, which is optimized by an EM-style algorithm. Moreover, We derive an efficient solver by alternating optimization of subproblems and exploit Monte Carlo sampling to escape from local minima. We demonstrate the effectiveness of PML-GMM through experiments on synthetic, eye-fixation, flow cytometry, and social check-in data.
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Wong, C. F., O. Odejimi, B. M. Conn, J. Davis, J. Ataiants, E. V. Fedorova, M. Suen, S. J. Lee, A. Osornio, and S. E. Lankenau. "Gender by Ethnicity Differences in Trajectory of Cannabis Use Among Cannabis-Using Young Adults during Pre- and Post-Recreational Cannabis Legalization (RCL) in Los Angeles." In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.22.

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Introduction: By the end of 2022, most states across the US except for three would have enacted some form of legalized cannabis policy. Support for the legalization of cannabis for recreational purposes are particularly high among young adults. Given the rapidly changing policy landscape, understanding how these policies may have impacted cannabis use among different groups of young people can help inform current and future policy decisions and programs/intervention to curb problematic use. There is evidence to suggest significant and meaningful differences in use behaviors among individuals from different racial/ethnic backgrounds and gender identities. However, limited research has examined how these groups based on the intersection of these identities might differ in their cannabis use prior to and after recreational cannabis legalization (RCL). Method: 366 cannabis-using young adults (aged 18-26) comprising 210 medical cannabis patients and 156 non-patients were surveyed annually between 2014-2020 in Los Angeles culminating into 6 waves of data. Bilinear spline growth curve models examined changes in cannabis use trajectory, with three waves pre-RCL and three waves post-RCL after accounting for patient status and age. Multi-group analyses investigated differences between six genderXrace/ethnicity subgroups: 1) African American Females (AAF); 2) Caucasian/White Females (WF); 3) Hispanic Females/Latina (HF); 4) African American Males (AAM); 5) Caucasian/White Males (WM); and 6) Hispanic Males/Latino (HM). Omnibus tests investigated homogeneity in the latent growth constructs across the 6 groups. We tested equality of covariances (correlations) and means across groups (p < .05). If inequality was shown, further tests were conducted. Results: Overall, significant group differences were observed in cannabis use trajectories and the correlations between intercepts and growth factors. Specifically, HF, HM, AAM and WM reported moderate level of cannabis use (between 50 to 56 days of use) compared to AAF and WF at baseline, whereby AAF reported significantly higher use (70.72 days) relative to all other groups. In contrast, WF reported significantly lower use (35.42 days). There were different patterns in pre-RCL growth parameters. Whereas AAF and HF had relatively flat rate of change, WF, WM, and HM had relatively similar significant decrease in use pre-RCL. Interestingly, during the period post-RCL, AAF, WM, and HM all showed significant decline in use, but WF was the only group with a significant increase in use while HF and AAM had modest increases in use. While baseline use generally predicted pre-RCL use within each subgroup (for some, baseline use led to more rapid increase while for others, it led to more rapid decrease in use), this is less true for post-RCL use. Significant effects associated with age and patient status were also observed. Conclusions: These are among the first findings to show how cannabis policy has differentially impacted cannabis use behaviors prior to and after RCL among a diverse population of cannabis-using young adults. Additional research should investigate potential mechanisms of these difference and longer-term health impacts.
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Britton, Mark, Eric Porges, Ronald Cohen, Yan Wang, Gladys Ibanez, Charurut Somboonwit, and Robert Cook. "Adolescent-Onset Cannabis Use Disorder Is Associated With Greater Self-Reported Apathy Among Adults Living with HIV in Florida." In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.41.

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INTRODUCTION Heavy cannabis use has been associated with increased self-reported apathy, or the reduction in motivation and goal-oriented behavior. Apathy is also prevalent in people living with HIV (PLWH). Cannabis use is prevalent among PLWH and has been associated with alterations in brain areas linked to motivation and reward. However, there is a paucity of studies directly examining heavy cannabis use as a predictor of apathy in this population. The current study focuses on age of initiating heavy use, as the neurobehavioral effects of chronic cannabis use may be intensified by early heavy use. We hypothesized that adolescent-onset heavy users would show greater apathy than adult-onset heavy users and that both groups would show greater apathy than never-heavy users and never-users. METHODS Baseline data were taken from a larger study of marijuana use, cognition, and health in adults living with HIV; included participants had complete marijuana use data (N = 236). The Marin Apathy Evaluation Scale – Self (AES-S) was used to measure self-reported apathy. The marijuana section of the Substance Abuse Module (SAM-5) was administered. Participants were divided, based on age of first meeting criteria for Cannabis Use Disorder, into early-onset (<18) CUD, late-onset CUD, never-CUD, and never-user groups. To account for variations in cell size and outliers, a robust one-way ANOVA was conducted using the WRS2 R package, with age of onset of CUD as a predictor and AES-S total score as dependent variable; results were submitted to Hochberg post-hoc tests. RESULTS The mean age of included participants was 49.81 years. 73% of participants identified as black/African American, and 54% were assigned male at birth. 8% of included participants had early-onset CUD; 29% had late-onset CUD; 43% never met criteria for CUD; and 20% never used marijuana. 71.6% of participants currently used marijuana at least once a week. The mean AES-S score was 29.81. Age of CUD onset predicted AES-S score, F(3,48.5)=5.84, p = 0.002. Post hoc tests revealed that the early-onset group (mean = 33.4) was significantly more apathetic than the never-user group (mean = 28.5) (Ψ = 5.95, CI=1.73-10.16, p = 0.002) and the never-CUD group (mean = 29.9) (Ψ = 4.02, CI = 0.60-7.43, p = 0.013). No difference was detected between late-onset (mean = 30.1), never-CUD, and never-user groups (p >.05). DISCUSSION We observed that age of Cannabis Use Disorder onset is associated with AES-S score among adults living with HIV, such that adolescent-onset Cannabis Use Disorder predicted higher levels of apathy relative to groups with no history of Cannabis Use Disorder or cannabis use. Two interpretations of this finding may be advanced: first, that individuals predisposed to apathy are more likely to engage in heavy substance use; second, that early-onset substance use alters behavior and perhaps underlying reward circuitry. Limitations of this study include the absence of a control group without HIV and the cross-sectional nature of our data. Future directions include assessing the roles of current age, depression, and HIV viral suppression as potential covariates.
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Reports on the topic "Latent Covariates"

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Bang, Minji, Wayne Gao, Andrew Postlewaite, and Holger Sieg. Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates. Cambridge, MA: National Bureau of Economic Research, February 2021. http://dx.doi.org/10.3386/w28436.

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