The Annals of Statistics

On a Simple Estimation Procedure for Censored Regression Models with Known Error Distributions

Leo Breiman, Yacov Tsur, and Amos Zemel

Full-text: Open access

Abstract

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 $\sqrt N$-consistent and asymptotically normal.

Article information

Source
Ann. Statist., Volume 21, Number 4 (1993), 1711-1720.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349394

Digital Object Identifier
doi:10.1214/aos/1176349394

Mathematical Reviews number (MathSciNet)
MR1245765

Zentralblatt MATH identifier
0790.62026

JSTOR
links.jstor.org

Subjects
Primary: 62F10: Point estimation
Secondary: 62F12: Asymptotic properties of estimators

Keywords
Censored data iterative least squares geometrical convergence

Citation

Breiman, Leo; Tsur, Yacov; Zemel, Amos. On a Simple Estimation Procedure for Censored Regression Models with Known Error Distributions. Ann. Statist. 21 (1993), no. 4, 1711--1720. doi:10.1214/aos/1176349394. https://projecteuclid.org/euclid.aos/1176349394


Export citation