BIVARIATE MARSHALL–OLKIN EXPONENTIAL SHOCK MODEL

The well-known Marshall–Olkin model is known for its extension of exponential distribution preserving lack of memory property. Based on shock models, a new generalization of the bivariate Marshall–Olkin exponential distribution is given. The proposed model allows wider range tail dependence which is...

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Bibliographic Details
Published inProbability in the engineering and informational sciences Vol. 35; no. 3; pp. 745 - 765
Main Authors Mohtashami-Borzadaran, H.A., Jabbari, H., Amini, M.
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 01.07.2021
Subjects
Algorithms
Bivariate analysis
Multivariate analysis
Personal computers
Probability distribution functions
Random variables
distortion copula
Marshall–Olkin model
shock model
stochastic comparison
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Summary:The well-known Marshall–Olkin model is known for its extension of exponential distribution preserving lack of memory property. Based on shock models, a new generalization of the bivariate Marshall–Olkin exponential distribution is given. The proposed model allows wider range tail dependence which is appealing in modeling risky events. Moreover, a stochastic comparison according to this shock model and also some properties, such as association measures, tail dependence and Kendall distribution, are presented. The new shock model is analytically quite tractable, and it can be used quite effectively, to analyze discrete–continuous data. This has been shown on real data. Finally, we propose the multivariate extension of the Marshall–Olkin model that has some intersection with the well-known multivariate Archimax copulas.
ISSN:0269-9648
1469-8951
DOI:10.1017/S0269964820000194