Finite Mixture Modeling via Skew-Laplace Birnbaum–Saunders Distribution

DOI

10.2991/jsta.d.200224.008How to use a DOI?

Keywords

Birnbaum–Saunders distribution; Normal mean-variance mixture model; Skew-Laplace distribution; Finite mixture model; ECM algorithm

Abstract

Finite mixture model is a widely acknowledged model-based clustering method for analyzing data. In this paper, a new finite mixture model via an extension of Birnbaum–Saunders distribution is introduced. The new mixture model provide a useful generalization of the heavy-tailed lifetime model since the mixing components cover both skewness and kurtosis. Some properties and characteristics of the model are derived and an expectation and maximization (EM)-type algorithm is developed to compute maximum likelihood estimates. The asymptotic standard errors of the parameter estimates are obtained via offering an information-based approach. Finally, the performance of the methodology is illustrated by considering both simulated and real datasets.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

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  - Mehrdad Naderi
AU  - Mahdieh Mozafari
AU  - Kheirolah Okhli
PY  - 2020
DA  - 2020/03/05
TI  - Finite Mixture Modeling via Skew-Laplace Birnbaum–Saunders Distribution
JO  - Journal of Statistical Theory and Applications
SP  - 49
EP  - 58
VL  - 19
IS  - 1
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.200224.008
DO  - 10.2991/jsta.d.200224.008
ID  - Naderi2020
ER  -