Globally Robust Confidence Intervals for Location

Abstract

Classical inference considers sampling variability to be the only source of uncertainty, and does not address the issue of bias caused by contamination. Naive robust intervals replace the classical estimates by their robust counterparts without considering the possible bias of the robust point estimates. Consequently, the asymptotic coverage proportion of these intervals of any nominal level will invariably tend to zero for any proportion of contamination.

In this study, we attempt to achieve reasonable coverage percentages by constructing globally robust confidence intervals that adjust for the bias of the robust point estimates. We improve these globally robust intervals by considering the direction of the bias of the robust estimates used. We compare the proposed intervals with the existing ones through an extensive simulation study. The proposed methods have reasonable coverage percentage while the existing method show very poor coverage as sample size increases.

DOI: http://dx.doi.org/10.3329/dujs.v60i1.10347  

Dhaka Univ. J. Sci. 60(1): 109-113 2012 (January)