Robustness via mixtures of exponential power distributions

Ehud Makov; Alon Hazan; Zinoviy Landsman

doi:10.1016/S0167-9473(02)00153-6

Robustness via mixtures of exponential power distributions

Ehud Makov, Alon Hazan, Zinoviy Landsman

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

A mixture of exponential power distributions (EPD) is suggested and is shown to possess robust qualities. The Gibbs sampler is applied for estimating the unknown parameters of the model and its algorithm is devised in order to allow a wide range of prior distributions for the unknown parameters. Numerical studies, using real financial data, demonstrate the effectiveness of the proposed model and a theoretical study explains the superiority of the EPD mixture over a normal mixture.

Original language	English
Pages (from-to)	111-121
Number of pages	11
Journal	Computational Statistics and Data Analysis
Volume	42
Issue number	1-2
DOIs	https://doi.org/10.1016/S0167-9473(02)00153-6
State	Published - 19 Feb 2003

Keywords

Adaptive rejection sampling algorithm
Exponential power distribution
Gibbs sampler
Mixture
Robustness

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/S0167-9473(02)00153-6

Cite this

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abstract = "A mixture of exponential power distributions (EPD) is suggested and is shown to possess robust qualities. The Gibbs sampler is applied for estimating the unknown parameters of the model and its algorithm is devised in order to allow a wide range of prior distributions for the unknown parameters. Numerical studies, using real financial data, demonstrate the effectiveness of the proposed model and a theoretical study explains the superiority of the EPD mixture over a normal mixture.",

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AU - Landsman, Zinoviy

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AB - A mixture of exponential power distributions (EPD) is suggested and is shown to possess robust qualities. The Gibbs sampler is applied for estimating the unknown parameters of the model and its algorithm is devised in order to allow a wide range of prior distributions for the unknown parameters. Numerical studies, using real financial data, demonstrate the effectiveness of the proposed model and a theoretical study explains the superiority of the EPD mixture over a normal mixture.

KW - Adaptive rejection sampling algorithm

KW - Exponential power distribution

KW - Gibbs sampler

KW - Mixture

KW - Robustness

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ER -

Robustness via mixtures of exponential power distributions

Abstract

Keywords

ASJC Scopus subject areas

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