A novel reversible jump algorithm for generalized linear models

• 成果类型: Article
• 署名作者: Papathomas, M.; Dellaportas, P.; Vasdekis, V. G. S.
• 署名单位: Imperial College London; Athens University of Economics & Business
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asq071
• 发表日期: 2011
• 页码: 231236
• 关键词: construction; computation

摘要

We propose a novel methodology to construct proposal densities in reversible jump algorithms that obtain samples from parameter subspaces of competing generalized linear models with differing dimensions. The derived proposal densities are not restricted to moves between nested models and are applicable even to models that share no common parameters. We illustrate our methodology on competing logistic regression and log-linear graphical models, demonstrating how our suggested proposal densities, together with the resulting freedom to propose moves between any models, improve the performance of the reversible jump algorithm.