Semiparametric inference in a partial linear model

• 成果类型: Article
• 署名作者: Bhattacharya, PK; Zhao, PL
• 署名单位: University of California System; University of California Davis; Merck & Company
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1997
• 页码: 244-262
• 关键词: efficient estimation; ADDITIVE REGRESSION; convergence-rates

摘要

In a partial linear model, the dependence of a response variate Y on covariates (W, X) is given by Y = W beta + eta(X)+ E, where E is independent of (W, X) with densities g and f, respectively. In this paper an asymptotically efficient estimator of beta is constructed solely under mild smoothness assumptions on the unknown eta, f and g, thereby removing the assumption of finite residual variance on which all least-squares-type estimators available in the literature are based.