Modified Maximum Likelihood Estimations of the Epsilon-Skew-Normal Family

DOI

10.2991/jsta.d.201208.001How to use a DOI?

Keywords

Asymmetry; EM-algorithm; Epsilon-skew-normal; Maximum likelihood estimates; Two-piece distributions

Abstract

In this work, maximum likelihood (ML) estimations of the epsilon-skew-normal (ESN) family are obtained using an EM-algorithm to modify the ordinary estimation already used and solve some of its problems within issues. This family can be used for analyzing the asymmetric and near-normal data, so the skewness parameter epsilon is the most important parameter among others. We have shown that the method has better performance compared to the method in G.S. Mudholkar, A.D. Hutson, J. Statist. Plann. Infer. 83 (2000), 291–309, especially in the strong skewness and small samples. Performances of the proposed ML estimates are shown via a simulation study and some real datasets under some statistical criteria as a way to illustrate the idea.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Parichehr Jamshidi
AU  - Mohsen Maleki
AU  - Zahra Khodadadi
PY  - 2020
DA  - 2020/12/14
TI  - Modified Maximum Likelihood Estimations of the Epsilon-Skew-Normal Family
JO  - Journal of Statistical Theory and Applications
SP  - 481
EP  - 486
VL  - 19
IS  - 4
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.201208.001
DO  - 10.2991/jsta.d.201208.001
ID  - Jamshidi2020
ER  -