Perfect sampling for Bayesian variable selection in a linear regression model |
| |
Institution: | 1. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore;2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China |
| |
Abstract: | We describe the use of perfect sampling algorithms for Bayesian variable selection in a linear regression model. Starting with a basic case solved by Huang and Djurić (EURASIP J. Appl. Si. Pr. 1 (2002) 38), where the model coefficients and noise variance are assumed to be known, we generalize the model step by step to allow for other sources of randomness. We specify perfect simulation algorithms that solve these cases by incorporating various techniques including Gibbs sampling, the perfect independent Metropolis–Hastings (IMH) algorithm, and recently developed “slice coupling” algorithms. Applications to simulated data sets suggest that our algorithms perform well in identifying relevant predictor variables. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|