Analyzing skewed financial data using skew scale-shap mixtures of multivariate normal distributions

Document Type : Special Issue Dedicated to memory of Prof. Mahbanoo Tata

Authors

1 Department of Statistics, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

2 Department of Statistics, Faculty of Basic Sciences, University of Hormozgan, Bandar Abbas, Iran.

Abstract

This paper introduces an innovative family of statistical models called the multivariate skew scale-shape mixtures of normal distributions. These models serve as a versatile tool in statistical analysis by efficiently characterizing the skewed and leptokurtic nature commonly observed in multivariate datasets. Their applicability shines in real-world scenarios where data often deviate from standard statistical assumptions due to the presence of outliers. We present an EM-type algorithm designed for maximizing likelihood estimation and evaluate the model's effectiveness through real-world data applications. Through rigorous testing against various datasets, we assess the performance and practicality of the proposed algorithm in real statistical scenarios. The results demonstrate the remarkable performance of this new family of distributions.

Keywords

Main Subjects

References

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Journal of Mahani Mathematical Research
Volume 13, Issue 3 - Serial Number 28
Special Issue Dedicated to Memory of Professor Mahbanoo Tata
August 2024
Pages 71-90

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History

  • Receive Date: 29 December 2023
  • Revise Date: 27 March 2024
  • Accept Date: 14 June 2024

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