title: Selection of latent variables for multiple mixed-outcome models.
abstract: Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknowna 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.
authors.affiliation: Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics.
authors.forename: Ling
authors.initials: L
authors.lastname: Zhou
authors.affiliation: Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics.
authors.forename: Huazhen
authors.initials: H
authors.lastname: Lin
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong.
authors.forename: Xinyuan
authors.initials: X
authors.lastname: Song
authors.affiliation: Department of Biostatistics University of Michigan.
authors.forename: Y I
authors.initials: YI
authors.lastname: Li
keywords: SCAD penalty; dependence structure; latent variables model; oracle property; selection of latent variables