A MISSING INFORMATION PRINCIPLE AND M-ESTIMATORS IN REGRESSION-ANALYSIS WITH CENSORED AND TRUNCATED DATA

• 成果类型: Article
• 署名作者: LAI, TL; YING, ZL
• 署名单位: Rutgers University System; Rutgers University New Brunswick
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176325627
• 发表日期: 1994
• 页码: 1222-1255
• 关键词: LINEAR-REGRESSION

摘要

A general missing information principle is proposed for constructing M-estimators of regression parameters in the presence df left truncation and right censoring on the observed responses. By making use of martingale central limit theorems and empirical process theory, the asymptotic normality of M-estimators is established under certain assumptions. Asymptotically efficient M-estimators are also developed by using data-dependent score functions. In addition, robustness properties of the estimators are discussed and formulas for their influence functions are derived for the robustness analysis.