Bayesian Analysis

Bayesian Semiparametric Inference on Functional Relationships in Linear Mixed Models

Seonghyun Jeong and Taeyoung Park

Full-text: Open access

Abstract

Regression models with varying coefficients changing over certain underlying covariates offer great flexibility in capturing a functional relationship between the response and other covariates. This article extends such regression models to include random effects and to account for correlation and heteroscedasticity in error terms, and proposes an efficient new data-driven method to estimate varying regression coefficients via reparameterization and partial collapse. The proposed methodology is illustrated with a simulated study and longitudinal data from a study of soybean growth.

Article information

Source
Bayesian Anal., Volume 11, Number 4 (2016), 1137-1163.

Dates
First available in Project Euclid: 30 November 2015

Permanent link to this document
https://projecteuclid.org/euclid.ba/1448852253

Digital Object Identifier
doi:10.1214/15-BA987

Mathematical Reviews number (MathSciNet)
MR3545476

Zentralblatt MATH identifier
1357.62172

Subjects
Primary: 62F15: Bayesian inference
Secondary: 62J99: None of the above, but in this section

Keywords
longitudinal data random effects model selection partial collapse panel data reparameterization

Citation

Jeong, Seonghyun; Park, Taeyoung. Bayesian Semiparametric Inference on Functional Relationships in Linear Mixed Models. Bayesian Anal. 11 (2016), no. 4, 1137--1163. doi:10.1214/15-BA987. https://projecteuclid.org/euclid.ba/1448852253


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