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December 2016 Bayesian Semiparametric Inference on Functional Relationships in Linear Mixed Models
Seonghyun Jeong, Taeyoung Park
Bayesian Anal. 11(4): 1137-1163 (December 2016). DOI: 10.1214/15-BA987

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.

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Seonghyun Jeong. Taeyoung Park. "Bayesian Semiparametric Inference on Functional Relationships in Linear Mixed Models." Bayesian Anal. 11 (4) 1137 - 1163, December 2016. https://doi.org/10.1214/15-BA987

Information

Published: December 2016
First available in Project Euclid: 30 November 2015

zbMATH: 1357.62172
MathSciNet: MR3545476
Digital Object Identifier: 10.1214/15-BA987

Subjects:
Primary: 62F15
Secondary: 62J99

Rights: Copyright © 2016 International Society for Bayesian Analysis

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Vol.11 • No. 4 • December 2016
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