Bayesian lasso for semiparametric structural equation models.

Biometrics
Ruixin GuoJoseph G Ibrahim

Abstract

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.

References

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Citations

Dec 18, 2013·Statistics in Medicine·Xinyuan SongXiangnan Feng
Aug 8, 2014·Frontiers in Psychology·Augustin Kelava, Holger Brandt
Sep 27, 2016·Biometrical Journal. Biometrische Zeitschrift·An-Min TangNian-Sheng Tang
Jan 31, 2019·Biostatistics·Klaus Kähler Holst, Esben Budtz-Jørgensen
Jun 15, 2019·Statistics in Medicine·Chunjie WangXiaogang Dong
May 3, 2019·The British Journal of Mathematical and Statistical Psychology·Ji Yeh Choi, Heungsun Hwang
Nov 30, 2018·Statistics in Medicine·Kai KangHongtu Zhu
Aug 12, 2021·Multivariate Behavioral Research·Zachary J Roman, Holger Brandt

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