PROFILE LIKELIHOOD AND CONDITIONALLY PARAMETRIC MODELS

• 成果类型: Article
• 署名作者: SEVERINI, TA; WONG, WH
• 署名单位: University of Chicago
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348889
• 发表日期: 1992
• 页码: 1768-1802
• 关键词: information; EFFICIENCY

摘要

In this paper, we outline a general approach to estimating the parametric component of a semiparametric model. For the case of a scalar parametric component, the method is based on the idea of first estimating a one-dimensional subproblem of the original problem that is least favorable in the sense of Stein. The likelihood function for the scalar parameter along this estimated subproblem may be viewed as a generalization of the profile likelihood for that parameter. The scalar parameter is then estimated by maximizing this ''generalized profile likelihood.'' This method of estimation is applied to a particular class of semiparametric models, where it is shown that the resulting estimator is asymptotically efficient.