A Note on Robustness of Normal Variance Estimators Under t-Distributions

[[abstract]]In many real life problems one assumes a normal model because the sample histogram looks unimodal, symmetric, and/or the standard tests like the Shapiro-Wilk test favor such a model. However, in reality, the assumption of normality may be misplaced since the normality tests often fail to detect departure from normality (especially for small sample sizes) when the data actually comes from slightly heavier tail symmetric unimodal distributions. For this reason it is important to see how the existing normal variance estimators perform when the actual distribution is a t-distribution with k degrees of freedom (d.f.) (t k -distribution). This note deals with the performance of standard normal variance estimators under the t k -distributions. It is shown that the relative ordering of the estimators is preserved for both the quadratic loss as well as the entropy loss irrespective of the d.f. and the sample size (provided the risks exist).[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[incitationindex]]SSCI[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]US