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

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.