Development of a Bayesian Model For Finite Mixture Regression of the Skew-Laplace Distribution

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Tarami B.; Sanjari Farsipour N.; Khosravi H.

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Journal Paper

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Journal: JOURNAL OF STATISTICAL SCIENCES Year:2025 | Volume:19 | Issue:2 Page(s): 375-397

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Information Journal Paper

Title

Development of a Bayesian Model For Finite Mixture Regression of the Skew-Laplace Distribution

Author(s)

Tarami B. | Sanjari Farsipour N. | Khosravi H. | Issue Writer Certificate

Keywords

Skew-Laplace distribution

Mixture regresion

Metropolis-Hastings algorithm.

Abstract

In many applications, observations have a skewness, an elongated shape, a heavy tail, a multi-mode structure, or a mixed distribution. Therefore, models based on the normal distribution cannot provide correct inferences under such conditions and can lead to biased estimators or increased variance. The Laplace distribution and its generalizations can be suitable alternatives in such situations due to their elongation, heavy tails, and skewness. On the other hand, in models based on mixed distributions, there is always a possibility that fewer samples are available from one or more components. Therefore, given the Bayesian approach's advantage in handling small samples, this research developed a Bayesian model to fit a finite mixed regression model with Skew-Laplace distributions and conducted a simulation study to assess its performance. Laplace has been compared in two approaches, frequentist and Bayesian. The results show that the Bayesian approach of the model is more effective than other models.

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