Statistical Inference for Scale Mixture Models via Mellin Transform Approach

2022/11/03 by Denis Belomestny, Ekaterina Morozova, Belomestny, Denis +3 · 2 citations
Computer Science · #62G05 #62G20 #FOS: Computer and information sciences #Methodology (stat.ME) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2211.01799

openalex publication_date 2022/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Goodness-of-fit testing from observations with multiplicative measurement error
• Deconvolution of arbitrary distribution functions and densities

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