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Bayesian Lasso for Semiparametric Structural Equation Models
Author(s) -
Guo Ruixin,
Zhu Hongtu,
Chow SyMiin,
Ibrahim Joseph G.
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2012.01751.x
Subject(s) - structural equation modeling , latent variable , markov chain monte carlo , semiparametric regression , bayesian probability , nonparametric statistics , model selection , mathematics , covariate , bayesian inference , econometrics , lasso (programming language) , computer science , statistics , world wide web
Summary There has been great interest in developing nonlinear structural equation models and associated statistical inference procedures, including estimation and model selection methods. In this paper a general semiparametric structural equation model (SSEM) is developed in which the structural equation is composed of nonparametric functions of exogenous latent variables and fixed covariates on a set of latent endogenous variables. A basis representation is used to approximate these nonparametric functions in the structural equation and the Bayesian Lasso method coupled with a Markov Chain Monte Carlo (MCMC) algorithm is used for simultaneous estimation and model selection. The proposed method is illustrated using a simulation study and data from the Affective Dynamics and Individual Differences (ADID) study. Results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model.