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Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models
- Authors
- Kim, Gwangsu; Choi, Taeryon
- Issue Date
- May-2019
- Publisher
- ELSEVIER INC
- Keywords
- B-spline; Convergence rate; Latent variable; Nonparametric statistics; Structural equation model
- Citation
- JOURNAL OF MULTIVARIATE ANALYSIS, v.171, pp.68 - 82
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF MULTIVARIATE ANALYSIS
- Volume
- 171
- Start Page
- 68
- End Page
- 82
- ISSN
- 0047-259X
- Abstract
- We study the asymptotic properties of nonparametric Bayesian structural equation models (SEMs). Under mild conditions, when adjusting nonparametric error distributions, the posteriors of Bayesian SEMs achieve the optimal convergence rate up to log n terms in the nonparametric means and nonlinear relationships of the latent variables. Furthermore, we consider quantile regressions of the error and latent variables in Bayesian SEMs, and we show posterior consistency in Bayesian quantile regression. The theoretical results are validated using simulation studies. (C) 2018 Elsevier Inc. All rights reserved.
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Related Researcher
- Choi, Tae ryon
- College of Political Science & Economics (Department of Statistics)