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

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 distri...

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Bibliographic Details
Published in:Computational statistics 2021-09, Vol.36 (3), p.2201-2230
Main Authors: Mahdavi, Abbas, Amirzadeh, Vahid, Jamalizadeh, Ahad, Lin, Tsung-I
Format: Article
Language:English
Subjects:
Algorithms
Asymptotic methods
Asymptotic properties
Convolution
Economic Theory/Quantitative Economics/Mathematical Methods
Estimates
Mathematics and Statistics
Maximum likelihood estimates
Maximum likelihood estimation
Normal distribution
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Representations
Skewed distributions
Statistics
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Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-021-01079-2