1
Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
2
Department of Statistics, Faculty of Mathematics and Computing, Higher Education Complex of Bam, Bam, Iran
3
Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
10.22034/jirss.2017.16.06
Abstract
This paper presents a new mixture model via considering the univariate skew Laplace distribution. The new model can handle both heavy tails and skewness and is multimodal. Describing some properties of the proposed model, we present a feasible EM algorithm for iteratively computing maximum likelihood estimates. We also derive the observed information matrix for obtaining the asymptotic standard error of parameter estimates. The finite sample properties of the obtained estimators as well as the consistency of the associated standard error of parameter estimates are investigated by a simulation study. We also demonstrate the flexibility and usefulness of the new model by analyzing real data example.
Okhli,K. , Mozafari,M. and Naderi,M. (2022). Skew Laplace Finite Mixture Modelling. Journal of the Iranian Statistical Society, 16(2), 97-110. doi: 10.22034/jirss.2017.16.06
MLA
Okhli,K. , , Mozafari,M. , and Naderi,M. . "Skew Laplace Finite Mixture Modelling", Journal of the Iranian Statistical Society, 16, 2, 2022, 97-110. doi: 10.22034/jirss.2017.16.06
HARVARD
Okhli K., Mozafari M., Naderi M. (2022). 'Skew Laplace Finite Mixture Modelling', Journal of the Iranian Statistical Society, 16(2), pp. 97-110. doi: 10.22034/jirss.2017.16.06
CHICAGO
K. Okhli, M. Mozafari and M. Naderi, "Skew Laplace Finite Mixture Modelling," Journal of the Iranian Statistical Society, 16 2 (2022): 97-110, doi: 10.22034/jirss.2017.16.06
VANCOUVER
Okhli K., Mozafari M., Naderi M. Skew Laplace Finite Mixture Modelling. JIRSS, 2022; 16(2): 97-110. doi: 10.22034/jirss.2017.16.06
Journal of the Iranian Statistical Society