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Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling

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  • Fatma Zehra Doğru
  • Olcay Arslan

Abstract

In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation–maximization (EM) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the EM algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples.

Suggested Citation

  • Fatma Zehra Doğru & Olcay Arslan, 2017. "Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(21), pages 10879-10896, November.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:21:p:10879-10896
    DOI: 10.1080/03610926.2016.1252400
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    Cited by:

    1. Michael P. B. Gallaugher & Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2022. "Multivariate cluster weighted models using skewed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 93-124, March.
    2. Yan Li & Chun Yu & Yize Zhao & Weixin Yao & Robert H. Aseltine & Kun Chen, 2022. "Pursuing sources of heterogeneity in modeling clustered population," Biometrics, The International Biometric Society, vol. 78(2), pages 716-729, June.
    3. Fatma Zehra Doğru & Olcay Arslan, 2021. "Finite mixtures of skew Laplace normal distributions with random skewness," Computational Statistics, Springer, vol. 36(1), pages 423-447, March.

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