ON A SIMPLE ESTIMATION PROCEDURE FOR CENSORED REGRESSION-MODELS WITH KNOWN ERROR DISTRIBUTIONS

• 成果类型: Article
• 署名作者: BREIMAN, L; TSUR, Y; ZEMEL, A
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; Ben-Gurion University of the Negev
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176349394
• 发表日期: 1993
• 页码: 1711-1720
• 关键词: LINEAR-REGRESSION; maximum-likelihood; em algorithm

摘要

A simple and tractable iterative least squares estimation procedure for censored regression models with known error distributions is analyzed. It is found to be equivalent to a well-defined Huber type M-estimate. Under a regularity condition, the algorithm converges geometrically to a unique solution. The resulting estimate is square-root N-consistent and asymptotically normal.