Parameterization and Bayesian modeling

• 成果类型: Article
• 署名作者: Gelman, A
• 署名单位: Columbia University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000000458
• 发表日期: 2004
• 页码: 537-545
• 关键词: multiple imputation; inference; distributions; uncertainty; expansion; selection; binary; em

摘要

Progress in statistical computation often leads to advances in statistical modeling. For example, it is surprisingly common that an existing model is reparameterized. solely, for computational purposes, but then this new configuration motivates a new family of models that is useful in applied statistics. One reason why this phenomenon may not have been noticed in statistics is that reparameterizations do not change the likelihood. In a Bayesian framework, however, a transformation of parameters typically suggests a new family of prior distributions. We discuss examples in censored and truncated data, mixture modeling. multivariate imputation, stochastic processes, and multilevel models.