Bayesian semiparametric inference on functional relationships in linear mixed models

Seonghyun Jeong; Taeyoung Park

doi:10.1214/15-BA987

Bayesian semiparametric inference on functional relationships in linear mixed models

Seonghyun Jeong, Taeyoung Park

Department of Applied Statistics

Research output: Contribution to journal › Article › peer-review

11 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 language	English
Pages (from-to)	1137-1163
Number of pages	27
Journal	Bayesian Analysis
Volume	11
Issue number	4
DOIs	https://doi.org/10.1214/15-BA987
Publication status	Published - 2016 Dec 1

Bibliographical note

Funding Information:
Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2056049). We also thank the editors and referees for their many helpful comments.

Publisher Copyright:
© 2016 International Society for Bayesian Analysis.

All Science Journal Classification (ASJC) codes

Statistics and Probability
Applied Mathematics

Access to Document

10.1214/15-BA987

Cite this

@article{ac98eadc6eb6424a96bdcffb4f7e8ba1,

title = "Bayesian semiparametric inference on functional relationships in linear mixed models",

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.",

author = "Seonghyun Jeong and Taeyoung Park",

note = "Funding Information: Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2056049). We also thank the editors and referees for their many helpful comments. Publisher Copyright: {\textcopyright} 2016 International Society for Bayesian Analysis.",

year = "2016",

month = dec,

day = "1",

doi = "10.1214/15-BA987",

language = "English",

volume = "11",

pages = "1137--1163",

journal = "Bayesian Analysis",

issn = "1936-0975",

publisher = "Carnegie Mellon University",

number = "4",

}

TY - JOUR

T1 - Bayesian semiparametric inference on functional relationships in linear mixed models

AU - Jeong, Seonghyun

AU - Park, Taeyoung

N1 - Funding Information: Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2056049). We also thank the editors and referees for their many helpful comments. Publisher Copyright: © 2016 International Society for Bayesian Analysis.

PY - 2016/12/1

Y1 - 2016/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84990922612&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84990922612&partnerID=8YFLogxK

U2 - 10.1214/15-BA987

DO - 10.1214/15-BA987

M3 - Article

AN - SCOPUS:84990922612

SN - 1936-0975

VL - 11

SP - 1137

EP - 1163

JO - Bayesian Analysis

JF - Bayesian Analysis

IS - 4

ER -

Bayesian semiparametric inference on functional relationships in linear mixed models

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this