Entropy-based test for generalized Gaussian distributions

Autor:	Mehmet Siddik Cadirci, Dafydd Evans, Nikolai Leonenko, Vitalii Makogin
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Statistics and Probability Computational Mathematics Computational Theory and Mathematics Applied Mathematics FOS: Mathematics Mathematics - Statistics Theory Statistics Theory (math.ST) 62G05 28D20 62G10
ISSN:	0167-9473
Popis:	The proof of consistency for the kth nearest neighbour distance estimator of the Shannon entropy for an arbitrary fixed is provided. It is constructed the non-parametric test of goodness-of-fit for a class of introduced generalised multivariate Gaussian distributions based on a maximum entropy principle. The theoretical results are followed by numerical studies on simulated samples. It is shown that increasing of k improves the power of the introduced goodness of fit tests. The asymptotic normality of the test statistics is experimentally proven.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d9f930a4ce8b5b00579b0206b28a37f https://orca.cardiff.ac.uk/id/eprint/149192/1/CEML_22.pdf Zobrazit plný text záznamu Full Text from ScienceDirect