Influence Diagnostics and Estimation Algorithms for Powell's SCLS.

This article studies influence diagnostics and estimation algorithms for Powell's symmetrically censored least squares estimator. The proposed measures of influence are based on one-step approximations to the analogous deletion diagnostics used in least squares regression and can be conveniently constructed using a Newton-type algorithm. Additionally, it is found that this algorithm can be used to substantially reduce the computational burden of the estimator. The results of the article are illustrated with an application.