Estimation of a Loss Function for Spherically Symmetric Distributions in the General Linear Model
研究了从球对称分布抽样时点估计的损失估计问题，在一般线性模型中比较了最小二乘估计和收缩估计的无偏损失估计与改进损失估计，结果适用于一大类分布且具有稳健性。
This paper is concerned with estimating the loss of a point estimator when sampling from a spherically symmetric distribution. We examine the canonical setting of a general linear model where the dimension of the parameter space is greater than 4 and less than the dimension of the sampling space. We consider two location estimators--the least squares estimator and a shrinkage estimator--and we compare their unbiased loss estimator with an improved loss estimator. The domination results are valid for a large class of spherically symmetric distributions and, in so far as the sampling distribution does not need to be precisely specified, the estimates have desirable robustness properties.