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

被引：4

作者：

BREIMAN, L

TSUR, Y

ZEMEL, A

机构：

[1] UNIV MINNESOTA,DEPT AGR & APPL ECON,ST PAUL,MN 55108

[2] BEN GURION UNIV NEGEV,JACOB BLAUSTEIN INST DESERT RES,IL-84993 BEER SHEVA,ISRAEL

来源：

ANNALS OF STATISTICS | 1993年 / 21卷 / 04期

关键词：

CENSORED DATA; ITERATIVE LEAST SQUARES; GEOMETRICAL CONVERGENCE;

D O I：

10.1214/aos/1176349394

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：1711 / 1720

页数：10

共 12 条

[1]

BUCKLEY J, 1979, BIOMETRIKA, V66, P429

[2] FITTING LINEAR-REGRESSION MODELS TO CENSORED-DATA BY LEAST-SQUARES AND MAXIMUM-LIKELIHOOD METHODS [J].