title: Structure detection of semiparametric structural equation models with Bayesian adaptive group lasso.
abstract: 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.
authors: Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
authors: Xiang-Nan
authors: XN
authors: Feng
authors: Guo-Chang
authors: GC
authors: Wang
authors: Yi-Fan
authors: YF
authors: Wang
authors: Xin-Yuan
authors: XY
authors: Song
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003928:Diabetic Nephropathies / Q000097:blood / Q000150:complications; D006723:Hong Kong; D006801:Humans; D015233:Models, Statistical; D051436:Renal Insufficiency, Chronic / Q000097:blood / Q000150:complications; D012307:Risk Factors; D018709:Statistics, Nonparametric
keywords: Bayesian adaptive group lasso; MCMC methods; basis expansion approximation; semiparametric SEMs