A unified approach to the calculation of information operators in semiparametric models

• 成果类型: Article
• 署名作者: Mao, Lu
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaa037
• 发表日期: 2020
• 页码: 983995
• 关键词: maximum-likelihood-estimation; transformation models; efficient estimation; ASYMPTOTIC THEORY; regression

摘要

The infinite-dimensional information operator for the nuisance parameter plays a key role in semiparametric inference, as it is closely related to the regular estimability of the target parameter. Calculation of information operators has traditionally proceeded in a case-by-case manner and has often entailed lengthy derivations with complicated arguments. We develop a unified framework for this task by exploiting commonality in the form of semiparametric likelihoods. The general formula developed allows one to derive information operators with simple calculus and, if necessary at all, a minimal amount of probabilistic evaluation. This streamlined approach shows its simplicity and versatility in application to a number of existing models as well as a new model of practical interest.