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 | |
Publication status | Published - 2016 Dec 1 |
Bibliographical note
Publisher Copyright:© 2016 International Society for Bayesian Analysis.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Applied Mathematics