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

Abbas Mahdavi (), Vahid Amirzadeh (), Ahad Jamalizadeh () and Tsung-I Lin ()
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Abbas Mahdavi: Shahid Bahonar University of Kerman
Vahid Amirzadeh: Shahid Bahonar University of Kerman
Ahad Jamalizadeh: Shahid Bahonar University of Kerman
Tsung-I Lin: National Chung Hsing University

Computational Statistics, 2021, vol. 36, issue 3, No 30, 2230 pages

Abstract: Abstract 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.

Keywords: Bessel function; EM-type algorithms; Exponential-power distribution; Scale-shape mixtures; Skew-symmetric distribution; Truncated normal distribution (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-021-01079-2

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