PMID- 25640461
OWN - NLM
STAT- MEDLINE
DCOM- 20160201
LR  - 20171116
IS  - 1097-0258 (Electronic)
IS  - 0277-6715 (Linking)
VI  - 34
IP  - 9
DP  - 2015 Apr 30
TI  - Structure detection of semiparametric structural equation models with Bayesian 
      adaptive group lasso.
PG  - 1527-47
LID - 10.1002/sim.6410 [doi]
AB  - 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.
CI  - Copyright (c) 2015 John Wiley & Sons, Ltd.
FAU - Feng, Xiang-Nan
AU  - Feng XN
AD  - Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong 
      Kong.
FAU - Wang, Guo-Chang
AU  - Wang GC
FAU - Wang, Yi-Fan
AU  - Wang YF
FAU - Song, Xin-Yuan
AU  - Song XY
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20150112
PL  - England
TA  - Stat Med
JT  - Statistics in medicine
JID - 8215016
SB  - IM
MH  - *Bayes Theorem
MH  - Bias
MH  - Biometry/methods
MH  - Computer Simulation
MH  - *Data Interpretation, Statistical
MH  - Diabetic Nephropathies/blood/complications
MH  - Hong Kong
MH  - Humans
MH  - *Models, Statistical
MH  - Renal Insufficiency, Chronic/blood/complications
MH  - Risk Factors
MH  - Statistics, Nonparametric
OTO - NOTNLM
OT  - Bayesian adaptive group lasso
OT  - MCMC methods
OT  - basis expansion approximation
OT  - semiparametric SEMs
EDAT- 2015/02/03 06:00
MHDA- 2016/02/02 06:00
CRDT- 2015/02/03 06:00
PHST- 2014/06/04 00:00 [received]
PHST- 2014/10/31 00:00 [revised]
PHST- 2014/12/16 00:00 [accepted]
PHST- 2015/02/03 06:00 [entrez]
PHST- 2015/02/03 06:00 [pubmed]
PHST- 2016/02/02 06:00 [medline]
AID - 10.1002/sim.6410 [doi]
PST - ppublish
SO  - Stat Med. 2015 Apr 30;34(9):1527-47. doi: 10.1002/sim.6410. Epub 2015 Jan 12.