Bayesian semiparametric inference on functional relationships in linear mixed models

Seonghyun Jeong, Taeyoung Park

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1137-1163
Number of pages27
JournalBayesian Analysis
Volume11
Issue number4
DOIs
Publication statusPublished - 2016 Dec 1

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Semiparametric Inference
Linear Mixed Model
Varying Coefficients
Functional Relationship
Covariates
Regression Model
Reparameterization
Heteroscedasticity
Soybean
Longitudinal Data
Regression Coefficient
Error term
Random Effects
Data-driven
Flexibility
Partial
Methodology
Estimate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Applied Mathematics

Cite this

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Bayesian semiparametric inference on functional relationships in linear mixed models. / Jeong, Seonghyun; Park, Taeyoung.

In: Bayesian Analysis, Vol. 11, No. 4, 01.12.2016, p. 1137-1163.

Research output: Contribution to journalArticle

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