MINIMUM HELLINGER-TYPE DISTANCE ESTIMATION FOR CENSORED-DATA

• 成果类型: Article
• 署名作者: YING, ZL
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348773
• 发表日期: 1992
• 页码: 1361-1390
• 关键词: random truncation; models

摘要

A Hellinger-type distance for hazard rate functions is defined. It is used to obtain a class of minimum distance estimators for data that are subject to a possible right censorship. The corresponding score process is shown to be approximated by a martingale, which is exploited to obtain the asymptotic normality under considerably weaker conditions than those normally assumed for minimum Hellinger distance estimators. It is also shown that under the parametric assumption the estimators are asymptotically as efficient as the maximum likelihood estimators.