Methods and criteria for model selection

• 成果类型: Review
• 署名作者: Kadane, JB; Lazar, NA
• 署名单位: Carnegie Mellon University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000000269
• 发表日期: 2004
• 页码: 279-290
• 关键词: bayesian variable selection; normalizing constants; INFLATION CRITERION; Graphical Models; CHOICE; regression; elicitation; bootstrap; inference

摘要

Model selection is an important part of any statistical analysis and, indeed, is central to the pursuit of science in general. Many authors have examined the question of model selection from both frequentist and Bayesian perspectives, and many tools for selecting the best model have been suggested in the literature. This paper considers the various proposals from a Bayesian decision-theoretic perspective.