Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions

Wan Lun Wang, Ahad Jamalizadeh, Tsung I. Lin

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Finite mixtures of multivariate skew distributions have become increasingly popular in recent years due to their flexibility and robustness in modeling heterogeneity, asymmetry and leptokurticness of the data. This paper introduces a novel finite mixture of multivariate scale-shape mixtures of skew-normal distributions to enhance strength and flexibility when modeling heterogeneous multivariate data that contain more extreme non-normal features. A computational tractable ECM algorithm which consists of analytically simple E- and CM-steps is developed to carry out maximum likelihood estimation of parameters. The asymptotic covariance matrix of parameter estimates is derived from the observed information matrix using the outer product of expected complete-data scores. We demonstrate the utility of the proposed approach through simulated and real data examples.

Original languageEnglish
Pages (from-to)2643-2670
Number of pages28
JournalStatistical Papers
Volume61
Issue number6
DOIs
Publication statusPublished - 2020 Dec 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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