Topic
Latent variable model
About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.
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TL;DR: This paper assume that the continuous latent variable (common factor) is related to an observed covariates through the multivariate linear regression model on the basis of traditional factor analyzers (FA) and EM algorithm is used to estimate model parameters.
Abstract: This paper assume that the continuous latent variable(common factor) is related to an observed covariates through the multivariate linear regression model on the basis of traditional factor analyzers(FA).EM algorithm is used to estimate model parameters.A detailed derivation of its is proposed in the context.
50 citations
TL;DR: In this article, a semiparametric latent variable model is developed, in which outcome latent variables are related to explanatory latent variables and covariates through an additive structural equation formulated by a series of unspecified smooth functions.
Abstract: This article aims to develop a semiparametric latent variable model, in which outcome latent variables are related to explanatory latent variables and covariates through an additive structural equation formulated by a series of unspecified smooth functions. The Bayesian P-splines approach, together with a Markov chain Monte Carlo algorithm, is proposed to estimate smooth functions, unknown parameters, and latent variables in the model. The performance of the developed methodology is demonstrated by a simulation study. An illustrative example in analyzing bone mineral density in older men is provided. An Appendix which includes technical details of the proposed MCMC algorithm and an R code in implementing the algorithm are available as the online supplemental materials.
50 citations
TL;DR: Of the 5 estimation methods, it was found that overall the methods based on maximum likelihood estimation and the Bayesian approach performed best in terms of bias, root-mean-square error, standard error ratios, power, and Type I error control, although key differences were observed.
Abstract: Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent moderated structural equation method, (d) a fully Bayesian approach, and (e) marginal maximum likelihood estimation. Of the 5 estimation methods, it was found that overall the methods based on maximum likelihood estimation and the Bayesian approach performed best in terms of bias, root-mean-square error, standard error ratios, power, and Type I error control, although key differences were observed. Similarities as well as disparities among methods are highlight and general recommendations articulated. As a point of comparison, all 5 approaches were fit to a reparameterized version of the latent quadratic model to educational reading data.
50 citations
TL;DR: In this article, a latent variable model is used to obtain a low-dimensional representation of the network in terms of node-specific network factors, and a novel testing procedure is introduced to determine if dependencies exist between the network factors.
Abstract: Network analysis is often focused on characterizing the dependencies between network relations and node-level attributes. Potential relationships are typically explored by modeling the network as a function of the nodal attributes or by modeling the attributes as a function of the network. These methods require specification of the exact nature of the association between the network and attributes, reduce the network data to a small number of summary statistics, and are unable to provide predictions simultaneously for missing attribute and network information. Existing methods that model the attributes and network jointly also assume the data are fully observed. In this article, we introduce a unified approach to analysis that addresses these shortcomings. We use a previously developed latent variable model to obtain a low-dimensional representation of the network in terms of node-specific network factors. We introduce a novel testing procedure to determine if dependencies exist between the network factor...
50 citations
TL;DR: It is suggested that symptoms of emotional distress seem to have the same factor structure across cultures, as judged by various statistical measures of fit.
Abstract: Background. Factor analysis has been employed to identify latent variables that are unifying constructs and that parsimoniously describe correlations among a related group of variables. Confirmatory factor analysis is used to test hypothesized factor structures for a set of variables; it can also, as in this paper be used to model data from two or more groups simultaneously to determine whether they have the same factor structure.Method. Non-psychotic psychiatric morbidity, elicited by the Revised Clinical Interview Schedule (CIS-R), from four culturally diverse populations was compared. Confirmatory factor analysis was employed to compare the factor structures of CIS-R data sets from Santiago, Harare, Rotherhithe and Ealing. These structures were compared with hypothetical one and two factor (depression–anxiety) models.Results. The models fitted well with the different data sets. The depression–anxiety model was marginally superior to the one factor model as judged by various statistical measures of fit. The two factors in depression–anxiety model were, however, highly correlated.Conclusions. The findings suggest that symptoms of emotional distress seem to have the same factor structure across cultures.
50 citations