Unknown

Dataset Information

0

Bayesian lasso for semiparametric structural equation models.


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.

SUBMITTER: Guo R 

PROVIDER: S-EPMC3371320 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

altmetric image

Publications

Bayesian lasso for semiparametric structural equation models.

Guo Ruixin R   Zhu Hongtu H   Chow Sy-Miin SM   Ibrahim Joseph G JG  

Biometrics 20120229 2


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 funct  ...[more]

Similar Datasets

| S-EPMC6075718 | biostudies-literature
| S-EPMC3577475 | biostudies-literature
| S-EPMC5557061 | biostudies-other
| S-EPMC6663110 | biostudies-literature
| S-EPMC8020077 | biostudies-literature
| S-EPMC3081790 | biostudies-literature
| S-EPMC8513775 | biostudies-literature
| S-EPMC3631538 | biostudies-literature
| S-EPMC6075844 | biostudies-literature
| S-EPMC7410097 | biostudies-literature