Simple relaxed conditional likelihood

• 成果类型: Article
• 署名作者: Hanfelt, John J.; Wang, Lijia
• 署名单位: Emory University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu028
• 发表日期: 2014
• 页码: 726732
• 关键词: projected score methods; nuisance parameters; ORTHOGONALITY; sensitivity; EFFICIENCY; models

摘要

When the data are sparse but not exceedingly so, we face a trade-off between bias and precision that makes the usual choice between conducting either a fully unconditional inference or a fully conditional inference unduly restrictive. We propose a method to relax the conditional inference that relies upon commonly available computer outputs. In the rectangular array asymptotic setting, the relaxed conditional maximum likelihood estimator has smaller bias than the unconditional estimator and smaller mean square error than the conditional estimator.