Дисертації з теми "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.
Повний текст джерелаRockwood, 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.
Повний текст джерелаWang, Junhua. "Large-Sample Logistic Regression with Latent Covariates in a Bayesian Networking Context." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/103.
Повний текст джерелаWang, Yan. "Covariates in Factor Mixture Modeling: Investigating Measurement Invariance across Unobserved Groups." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7715.
Повний текст джерелаHarman, David M. "Stochastic process customer lifetime value models with time-varying covariates." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2221.
Повний текст джерелаFlory, 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.
Повний текст джерелаHatzinger, 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.
Повний текст джерелаSeries: Research Report Series / Department of Statistics and Mathematics
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.
Повний текст джерелаWe 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
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.
Повний текст джерелаPereira, 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.
Повний текст джерелаFinanciadora de Estudos e Projetos
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.
Gaasch, Jean-Christoph Verfasser], Susanne [Akademischer Betreuer] [Rässler, and Claus [Akademischer Betreuer] Carstensen. "Bayesian estimation of latent trait distributions considering hierarchical structures and partially missing covariate data / Jean-Christoph Gaasch ; Susanne Rässler, Claus Carstensen." Bamberg : Otto-Friedrich-Universität Bamberg, 2017. http://d-nb.info/1147756945/34.
Повний текст джерелаGaasch, Jean-Christoph [Verfasser], Susanne [Akademischer Betreuer] Rässler, and Claus H. [Akademischer Betreuer] Carstensen. "Bayesian estimation of latent trait distributions considering hierarchical structures and partially missing covariate data / Jean-Christoph Gaasch ; Susanne Rässler, Claus Carstensen." Bamberg : Otto-Friedrich-Universität Bamberg, 2017. http://d-nb.info/1147756945/34.
Повний текст джерелаHori, Kazuki. "Disaggregating Within-Person and Between-Person Effects in the Presence of Linear Time Trends in Time-Varying Predictors: Structural Equation Modeling Approach." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103624.
Повний текст джерелаDoctor of Philosophy
Educational researchers are often interested in longitudinal phenomena within a person and relations between the person's characteristics. Since repeatedly measured variables reflect their within- and between-person aspects, researchers need to disaggregate them statistically to understand the phenomenon of interest. Recent studies found that the traditional centering method, where the individual's average of a predictor was subtracted from the original predictor value, could not correctly disentangle the within- and between-person effects when the predictor showed a systematic change over time (i.e., trend). They proposed some techniques to remove the trend; however, the detrending methods were only applicable to multilevel models. Therefore, the present study develops novel detrending methods using structural equation modeling. The proposed models are compared to the existing methods through a series of Monte Carlo simulations, where we can manipulate a data-generating model and its parameter values. The results indicate that (a) model misspecification for the time-varying predictor or outcome leads to systematic deviation of the estimates from their true values, (b) statistical properties of estimates of the effects are mostly determined by the type of between-person predictors (i.e., observed or latent), and (c) the latent predictor models require nonzero growth factor variances for unbiased estimation, while the observed predictor models need either nonzero or zero variance, depending on the parameter. As concluding remarks, some recommendations for the practitioners are provided.
Jay, Flora. "Méthodes bayésiennes pour la génétique des populations : relations entre structure génétique des populations et environnement." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00648601.
Повний текст джерела"Testing the Limits of Latent Class Analysis." Master's thesis, 2012. http://hdl.handle.net/2286/R.I.14788.
Повний текст джерелаDissertation/Thesis
M.A. Psychology 2012
Wang, Zijian Gerald. "On the Use of Covariates in a Latent Class Signal Detection Model, with Applications to Constructed Response Scoring." Thesis, 2012. https://doi.org/10.7916/D8DB87ZP.
Повний текст джерелаShen, Hua. "Statistical Methods for Life History Analysis Involving Latent Processes." Thesis, 2014. http://hdl.handle.net/10012/8496.
Повний текст джерелаPurnomo, Jerry Dwi Trijoyo, and 溥杰瑞. "A Modified Generalized Estimating Equation (GEE) Approach for Latent Class Models with Covariate Effects on Measured and Underlying Variables." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/t79rdw.
Повний текст джерела國立交通大學
統計學研究所
106
Recently, the regression extension of latent class analysis (RLCA) models have played an important role in many fields of research. RLCA models establish the relationship between primary covariates and latent class membership as well as the mediated direct effect of secondary covariates on measured responses. They have proven helpful for analyzing the relationship between measured multiple responses and covariates of interest. In this paper, we propose a generalized estimating equation (GEE) approach for the parameter estimation of RLCA models. This approach allows the specification of a working covariance that can ease the specification of the true covariance structure. We detail several structures of working covariance, iterative algorithms of Gauss-Newton methods for parameter estimation, and procedures for obtaining covariances of parameter estimators. An analysis of variables that probably affect the frailty of patients with cancer is used for illustration.