Maximum likelihood estimation for scale-shape mixtures of flexible generalized skew normal distributions via selection representation

Autor:	Vahid Amirzadeh, Ahad Jamalizadeh, Abbas Mahdavi, Tsung-I Lin
Rok vydání:	2021
Předmět:	Statistics and Probability Normal distribution Computational Mathematics Scale (ratio) Computer science Monte Carlo method Probabilistic logic Skew Statistics Probability and Uncertainty Representation (mathematics) Algorithm Selection (genetic algorithm) Convolution
Zdroj:	Computational Statistics. 36:2201-2230
ISSN:	1613-9658 0943-4062
Popis:	A scale-shape mixtures of flexible generalized skew normal (SSMFGSN) distributions is proposed as a novel device for modeling asymmetric data. Computationally feasible EM-type algorithms derived from the selection mechanism are presented to compute maximum likelihood (ML) estimates of SSMFGSN distributions. Some characterizations and probabilistic properties of the SSMFGSN distributions are also studied. Monte Carlo simulations show that the proposed estimating procedures can provide desirable asymptotic properties of the ML estimates and demand less computational burden in comparison with other existing algorithms based on convolution representations. The usefulness of the proposed methodology is illustrated by analyzing a real dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e09e9c938b7da1fa1ae234515d39497c https://doi.org/10.1007/s00180-021-01079-2 Zobrazit plný text záznamu Full text from SpringerLink