Finite mixtures of skew Laplace normal distributions with random skewness

In this paper, the shape mixtures of the skew Laplace normal (SMSLN) distribution is introduced as a flexible extension of the skew Laplace normal distribution which is also a heavy-tailed distribution. The SMSLN distribution includes an extra shape parameter, which controls skewness and kurtosis. S...

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Bibliographic Details
Published inComputational statistics Vol. 36; no. 1; pp. 423 - 447
Main Authors Doğru, Fatma Zehra, Arslan, Olcay
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
Springer Nature B.V
Subjects
Algorithms
Economic Theory/Quantitative Economics/Mathematical Methods
Kurtosis
Mathematics and Statistics
Maximum likelihood estimators
Normal distribution
Original Paper
Parameter estimation
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Skewed distributions
Skewness
Statistics
Finite mixture model
EM algorithm
SMSLN
ML
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Summary:In this paper, the shape mixtures of the skew Laplace normal (SMSLN) distribution is introduced as a flexible extension of the skew Laplace normal distribution which is also a heavy-tailed distribution. The SMSLN distribution includes an extra shape parameter, which controls skewness and kurtosis. Some distributional properties of this distribution are derived. Besides, we propose finite mixtures of SMSLN distributions to model both skewness and heavy-tailedness in heterogeneous data sets. The maximum likelihood estimators for parameters of interests are obtained via the expectation–maximization algorithm. We also give a simulation study and examine a real data example for the numerical illustration of proposed estimators.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-020-01025-8