A Kernel Scale Mixture of the Skew-Normal Distribution

Mahdi Salehi (), Andriette Bekker () and Mohammad Arashi ()
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Mahdi Salehi: University of Neyshabur, Department of Mathematics and Statistics
Andriette Bekker: University of Pretoria, Centre for Environmental Studies, Department of Geography, Geoinformatics and Meteorology
Mohammad Arashi: Ferdowsi University of Mashhad, Department of Statistics

A chapter in Flexible Nonparametric Curve Estimation, 2024, pp 269-278 from Springer

Abstract: Abstract The authors of this chapter provide a semi-parametric model for the skew-normal distribution by employing a kernel density estimator (KDE) as the mixing distribution. The objective of this strategy is to control the kurtosis of the distribution while maintaining the favorable characteristics of both the skew-normal density and the KDE. The importance of creating a semi-parametric skew-model is highlighted by simulation studies and a real data example. This model retains the key characteristics of the skew-normal distribution while including extra information from auxiliary observations.

Keywords: Auxiliary information; Kernel density estimator; Random walk sampler; Scale mixture distributions; Skew-normal distribution (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-66501-1_11

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DOI: 10.1007/978-3-031-66501-1_11

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