Statistical Inference for Scale Mixture Models via Mellin Transform Approach

Fuente: arXiv
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Autores principales: Belomestny, Denis, Morozova, Ekaterina, Panov, Vladimir
Formato: Preprint
Publicado: 2022
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author Belomestny, Denis
Morozova, Ekaterina
Panov, Vladimir
author_facet Belomestny, Denis
Morozova, Ekaterina
Panov, Vladimir
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2211_01799
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Statistical Inference for Scale Mixture Models via Mellin Transform Approach
Belomestny, Denis
Morozova, Ekaterina
Panov, Vladimir
Methodology
62G05, 62G20
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.
title Statistical Inference for Scale Mixture Models via Mellin Transform Approach
topic Methodology
62G05, 62G20
url https://arxiv.org/abs/2211.01799