Bayesian Lasso for Semiparametric Structural Equation Models

Ruixin Guo, Hongtu Zhu, Sy Miin Chow, Joseph G. Ibrahim

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)567-577
Number of pages11
Issue number2
StatePublished - Jun 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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