Influence Diagnostics and Estimation Algorithms for Powell's SCLS
研究鲍威尔对称截断最小二乘估计的影响诊断方法，基于一步近似构造诊断量，并利用牛顿型算法降低计算负担，通过实例展示结果。
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.