Authors

• Kheirolah Okhli ¹
• Mahdieh Mozafari ²
• Mehrdad Naderi ³

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

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.  

Keywords

• em algorithm
• finite mixture model
• Mean-variance mixture distribution
• Skew Laplace distribution