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.

Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models: Some New Findings

Abstract: The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data Differences between both approaches were found as well as similarities not found in earlier studies Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of tim

Expanding test–retest designs to include developmental time-lag components.

Abstract: Test-retest data can reflect systematic changes over varying intervals of time in a "time-lag" design. This article shows how latent growth models with planned incomplete data can be used to separate psychometric components of developmental interest, including internal consistency reliability, test-practice effects, factor stability, factor growth, and state fluctuation. Practical analyses are proposed using a structural equation model for longitudinal data on multiple groups with different test-retest intervals. This approach is illustrated using 2 sets of data collected from students measured on the Woodcock-Johnson—Revised Memory and Reading scales. The results show how alternative time-lag models can be fitted and interpreted with univariate, bivariate, and multivariate data. Benefits, limitations, and extensions of this structural time-lag approach are discussed.

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.

Abstract: Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.

The Impact of Specification Error on the Estimation, Testing, and Improvement of Structural Equation Models.

Abstract: The purpose of this paper is to assess the impact of misspecification on the estimation, testing, and improvement of structural equation models. A population study is conducted whereby a prototypical latent variable model is misspecified in various ways. Measurement model and structural model misspecifications are considered separately and together. The maximum likelihood estimator (ML) is compared to a limited information two-stage least squares (2SLS) estimator implemented in LISREL. The ratio of chi-square to its degrees of freedom and power of the likelihood ratio test is assessed for each misspecification. The modification index provided by LISREL is also studied. Results indicate that ML and 2SLS estimates of measurement and structural parameters are both affected by measurement model misspecification. For misspecification of the structural part, ML is shown to propagate errors throughout the structural parameters whereas 2SLS isolates errors only in the parameters of the misspecified equation. Results also show that relying on the ratio of chi-square to degrees of freedom as an index of fit may lead to accepting models with severe parameter bias. Finally, the modification index is shown to be an unreliable indicator of the location of a specification error.

Modeling the processes of development via latent variable growth curve methodology

Abstract: Recent advances in statistical methodology, in particular, latent growth modeling, allow for the testing of complex models regarding developmental trends from both an inter‐ and intraindividual perspective. An example application of latent growth curve methodology, analyzing the effects of gender and parental monitoring on developmental change in adolescent alcohol consumption, is presented. Furthermore, the analyses are conducted within a cohort‐sequential design, incorporating an approach to the analysis of missing data due to attrition. Findings are discussed with particular reference to the utility of latent growth curve models for assessing developmental processes at both the inter‐and intraindividual level across a variety of behavioral domains.