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Selection of Latent Variables for Multiple Mixed‐outcome Models
Author(s) -
Zhou Ling,
Lin Huazhen,
Song Xinyuan,
Li Yi
Publication year - 2014
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12084
Subject(s) - latent variable , latent class model , econometrics , latent variable model , estimator , inference , outcome (game theory) , mathematics , local independence , oracle , variable (mathematics) , model selection , structural equation modeling , a priori and a posteriori , selection (genetic algorithm) , statistics , computer science , machine learning , artificial intelligence , mathematical economics , mathematical analysis , philosophy , software engineering , epistemology
Latent variable models have been widely used for modelling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori , the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them.