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.

Supervised Nonlinear Dynamic System for Soft Sensor Application Aided by Variational Auto-Encoder

Abstract: Dynamic data modeling has been attracting much attention from researchers and has been introduced into the probabilistic latent variable model in the process industry. It is a huge challenge to extend these dynamic probabilistic latent variable models to nonlinear forms. In this article, a supervised nonlinear dynamic system (NDS) based on variational auto-encoder (VAE) is introduced for processes with dynamic behaviors and nonlinear characteristics. Based on the framework of VAE, which has a probabilistic data representation and a high fitting ability, the supervised NDS can extract effective nonlinear features for latent variable regression. The feasibility of the proposed supervised NDS is tested on two numerical examples and an industrial case. Detailed comparisons verify the effectiveness and superiority of the proposed model.

Do Different Response Formats Change the Latent Structure of Responses? an Empirical Investigation Using Taxometric Analysis

Abstract: Although Web-based surveys are increasing in popularity, very little research has been conducted on the psychometric implications of using different user interfaces for eliciting responses to survey items. The purpose of this study was to compare the latent structures of responses to two different user interface response formats in a Web-based survey. Two different coherent cut kinetics taxometric procedures—MAMBAC and L-mode factor analysis—were used to examine the latent structure of responses to a survey of library service quality using an unnumbered slider-bar user interface versus a radiobutton user interface. Strong evidence was found for a pure dimensional latent structure of responses from both user interfaces, which replicated across both taxometric procedures and across multiple survey items. It is concluded that the slider-bar and radiobutton user interfaces both yield similar latent structures of survey item responses. The implications of these findings for the construction of Web-based survey...

Rating scale analysis with latent class models

Abstract: A general approach for analyzing rating data with latent class models is described, which parallels rating models in the framework of latent trait theory. A general rating model as well as a two-parameter model with location and dispersion parameters, analogous to Andrich's Dislocmodel are derived, including parameter estimation via the EM-algorithm. Two examples illustrate the application of the models and their statisticalcontrol. Model restrictions through equality constrains are discussed and multiparameter generalizations are outlined.

Bounded-Influence Robust Estimation in Generalized Linear Latent Variable Models

Abstract: Latent variable models are used for analyzing multivariate data. Recently, generalized linear latent variable models for categorical, metric, and mixed-type responses estimated via maximum likelihood (ML) have been proposed. Model deviations, such as data contamination, are shown analytically, using the influence function and through a simulation study, to seriously affect ML estimation. This article proposes a robust estimator that is made consistent using the basic principle of indirect inference and can be easily numerically implemented. The performance of the robust estimator is significantly better than that of the ML estimators in terms of both bias and variance. A real example from a consumption survey is used to highlight the consequences in practice of the choice of the estimator.

An Extension of the Two-Parameter Logistic Model to the Multidimensional Latent Space.

Abstract: : A multidimensional extension of the two-parameter logistics latent trait model is presented and some of its characteristics are discussed. In addition, sufficient statistics for the parameters of the model are derived, as is the information function. Finally, the estimation of the parameters of the model using the maximum likelihood estimation technique is also discussed. (Author)