Ignorability for categorical data

• 成果类型: Article
• 署名作者: Jaeger, M
• 署名单位: Aalborg University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053605000000363
• 发表日期: 2005
• 页码: 1964-1981
• 关键词: incomplete-data; probabilities; likelihood; inference; models

摘要

We study the problem of ignorability in likelihood-based inference from incomplete categorical data. Two versions of the coarsened at random assumption (car) are distinguished, their compatibility with the parameter distinctness assumption is investigated and several conditions for ignorability that do not require an extra parameter distinctness assumption are established. It is shown that car assumptions have quite different implications depending on whether the underlying complete-data model is saturated or parametric. In the latter case, car assumptions can become inconsistent with observed data.