Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions

Creative Commons License

Dogru F. Z., Yu K., ARSLAN O.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.89, sa.17, ss.3213-3240, 2019 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 89 Sayı: 17
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/00949655.2019.1658111
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3213-3240
  • Anahtar Kelimeler: EM algorithm, joint location, scale and skewness models, mixture model, ML estimation, SLN, SN, MODELING VARIANCE HETEROGENEITY, MAXIMUM-LIKELIHOOD, VARIABLE SELECTION, SCALE-PARAMETERS, JOINT LOCATION, CONSISTENCY
  • Ankara Üniversitesi Adresli: Evet

Özet

Joint modelling skewness and heterogeneity is challenging in data analysis, particularly in regression analysis which allows a random probability distribution to change flexibly with covariates. This paper, based on a skew Laplace normal (SLN) mixture of location, scale, and skewness, introduces a new regression model which provides a flexible modelling of location, scale and skewness parameters simultaneously. The maximum likelihood (ML) estimators of all parameters of the proposed model via the expectation-maximization (EM) algorithm as well as their asymptotic properties are derived. Numerical analyses via a simulation study and a real data example are used to illustrate the performance of the proposed model.