EFFICIENT ESTIMATES IN SEMIPARAMETRIC ADDITIVE REGRESSION-MODELS WITH UNKNOWN ERROR DISTRIBUTION

• 成果类型: Article
• 署名作者: CUZICK, J
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348675
• 发表日期: 1992
• 页码: 1129-1136
• 关键词: partly linear-model; convergence-rates

摘要

Several authors have shown how to efficiently estimate-beta in the semiparametric additive model y = x'-beta + g(t) + error, g(t) smooth but unknown when the error distribution is normal. However, the general theory suggests that efficient estimation should be possible for general error distributions with finite Fisher information even when the error distribution is unknown. In this note we construct a sequence of estimators which achieves this goal under technical assumptions.