Finite mixtures of skew Laplace normal distributions with random skewness

Dogru F. Z., ARSLAN O.

COMPUTATIONAL STATISTICS, cilt.36, sa.1, ss.423-447, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s00180-020-01025-8
  • Dergi Adı: COMPUTATIONAL STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.423-447
  • Anahtar Kelimeler: EM algorithm, Finite mixture model, ML, SMSLN, MAXIMUM-LIKELIHOOD-ESTIMATION, IDENTIFIABILITY, INFERENCE, SCALE, EM
  • Ankara Üniversitesi Adresli: Evet

Özet

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.