Statistical inference for scale mixture models via Mellin transform approach

Abstract

This paper deals with statistical inference for the scale mixture models. We study an estimation approach based on the Mellin–Stieltjes transform that can be applied to both discrete and absolute continuous mixing distributions. The accuracy of the corresponding estimate is analysed in terms of its expected pointwise error. As an important technical result, we prove the analogue of the Berry–Esseen inequality for the Mellin transforms. The proposed statistical approach is illustrated by numerical examples.