International Journal of Nonlinear Analysis and Applications

Static finite mixture model of multivariate skew-normal distributions to cluster multivariatetime series based on generalized autoregressive score approach

Document Type : Research Paper

Authors

1 Science and Research Branch, Islamic Azad University, Tehran, Iran

2 School of Mathematics, Iran University of Science and Technology, Tehran, Iran

Abstract

This paper proposes an observation-driven finite mixture model for clustering high-dimension data. A simple algorithm using static hidden variables statically clusters the data into separate model components. The model accommodates normal and skew-normal distributed mixtures with time-varying component means, covariance matrices and skewness coefficient. These parameters are estimated using the EM algorithm and updated with the Generalized Autoregressive Scale (GAS) approach. Our proposed model is preferably clustered using a skew-normal distribution rather than a normal distribution when dealing with real data that may be skewed and asymmetrical. Finally, our proposed model will be evaluated using a simulation study and the results will be discussed using a real data set.

Keywords

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International Journal of Nonlinear Analysis and Applications
Volume 16, Issue 4
April 2025
Pages 27-39
  • Receive Date: 17 May 2021
  • Revise Date: 25 September 2021
  • Accept Date: 24 October 2021