A0473
Title: A sparse empirical Bayes approach to high-dimensional Gaussian process-based varying coefficient models
Authors: Myungjin Kim - Kyungpook National University (Korea, South) [presenting]
Gyuhyeong Goh - Kyungpook National University (Korea, South)
Abstract: Despite the increasing importance of high-dimensional varying coefficient models, the study of their Bayesian versions is still in its infancy. The contribution to the literature is by developing a sparse empirical Bayes formulation that addresses the problem of high-dimensional model selection in the framework of Bayesian varying coefficient modeling under Gaussian process (GP) priors. To break the computational bottleneck of GP-based varying coefficient modeling, a low-cost computation strategy that incorporates linear algebra techniques and the Laplace approximation into the evaluation of the high-dimensional posterior model distribution is introduced. A numerical study is conducted to demonstrate the superiority of the proposed Bayesian method compared to an existing high-dimensional varying coefficient modeling approach.
