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Structure detection of semiparametric structural equation models with Bayesian adaptive group lasso
Author(s) -
Feng XiangNan,
Wang GuoChang,
Wang YiFan,
Song XinYuan
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6410
Subject(s) - structural equation modeling , latent variable , bayesian probability , nonparametric statistics , lasso (programming language) , computer science , econometrics , semiparametric regression , mathematics , semiparametric model , artificial intelligence , machine learning , world wide web
Structural equation models (SEMs) are widely recognized as the most important statistical tool for assessing the interrelationships among latent variables. This study develops a Bayesian adaptive group least absolute shrinkage and selection operator procedure to perform simultaneous model selection and estimation for semiparametric SEMs, wherein the structural equation is formulated using the additive nonparametric functions of observed and latent variables. We propose the use of basis expansions to approximate the unknown functions. By introducing adaptive penalties to the groups of basis expansions, the nonlinear, linear, or non‐existent effects of observed and latent variables in the structural equation can be automatically detected. A simulation study demonstrates that the proposed method performs satisfactorily. This paper presents an application of revealing the observed and latent risk factors of diabetic kidney disease. Copyright © 2015 John Wiley & Sons, Ltd.