ASYMPTOTICALLY EFFICIENT ESTIMATION IN SEMIPARAMETRIC GENERALIZED LINEAR-MODELS

• 成果类型: Article
• 署名作者: CHEN, H
• 署名单位: National Taiwan University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176324700
• 发表日期: 1995
• 页码: 1102-1129
• 关键词: CONVERGENCE-RATES; regression

摘要

We use the method of maximum likelihood and regression splines to derive estimates of the parametric and nonparametric components of semiparametric generalized linear models. The resulting estimators of both components are shown to be consistent. Also, the asymptotic theory for the estimator of the parametric component is derived, indicating that the parametric component can be estimated efficiently without under-smoothing the nonparametric component.