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.

A recurrent latent variable model for sequential data

Abstract: In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics.

The Structure and Stability of Common Mental Disorders: The NEMESIS Study

Abstract: Background We analyzed the underlying latent structure of 12-month DSM-III-R diagnoses of 9 common disorders for the general population in the Netherlands. In addition, we sought to establish (1) the stability of the latent structure underlying mental disorders across a 1-year period (structural stability) and (2) the stability of individual differences in mental disorders at the level of the latent dimensions (differential stability). Methods Data were obtained from the first and second measurement of the Netherlands Mental Health Survey and Incidence Study (NEMESIS) (response rate at baseline: 69.7%, n = 7076; 1 year later, 79.4%, n = 5618). Nine common DSM-III-R diagnoses were assessed twice with the Composite International Diagnostic Interview with a time lapse of 1 year. Using structural equation modeling, the number of latent dimensions underlying these diagnoses was determined, and the structural and differential stability were assessed. Results A 3-dimensional model was established as having the best fit: a first dimension underlying substance use disorders (alcohol dependence, drug dependence); a second dimension for mood disorders (major depression, dysthymia), including generalized anxiety disorder; and a third dimension underlying anxiety disorders (simple phobia, social phobia, agoraphobia, and panic disorder). The structural stability of this model during a 1-year period was substantial, and the differential stability of the 3 latent dimensions was considerable. Conclusions Our results confirm the 3-dimensional model for 12-month prevalence of mental disorders. Results underline the argument for focusing on core psychopathological processes rather than on their manifestation as distinguished disorders in future population studies on common mental disorders.

Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach.

Abstract: Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to two commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudo-class draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to either classify-analyze technique, particularly when the association between the latent class variable and the distal outcome is strong. In addition, we show that only the model-based approach is consistent. The approach is demonstrated empirically: latent classes of adolescent depression are used to predict smoking, grades, and delinquency. SAS syntax for implementing this approach using PROC LCA and a corresponding macro are provided.

Linear structural equations with latent variables

Abstract: An interdependent multivariate linear relations model based on manifest, measured variables as well as unmeasured and unmeasurable latent variables is developed. The latent variables include primary or residual common factors of any order as well as unique factors. The model has a simpler parametric structure than previous models, but it is designed to accommodate a wider range of applications via its structural equations, mean structure, covariance structure, and constraints on parameters. The parameters of the model may be estimated by gradient and quasi-Newton methods, or a Gauss-Newton algorithm that obtains least-squares, generalized least-squares, or maximum likelihood estimates. Large sample standard errors and goodness of fit tests are provided. The approach is illustrated by a test theory model and a longitudinal study of intelligence.

Sampling Weights in Latent Variable Modeling

Abstract: This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several latent variable models including factor analysis with continuous and categorical indicators, latent class analysis, and growth models. The pseudomaximum likelihood estimation method is reviewed and illustrated with stratified cluster sampling. Additionally, the weighted least squares method for estimating structural equation models with categorical and continuous outcomes implemented in Mplus extended to incorporate sampling weights is also illustrated. The performance of several chi-square tests under unequal probability sampling is evaluated. Simulation studies compare the methods used in several statistical packages such as Mplus, HLM, SAS Proc Mixed, MLwiN, and the weighted sample statistics method used in other software packages.