THE JOINT DISTRIBUTION OF BIVARIATE EXPONENTIAL UNDER LINEARLY RELATED MODEL

Norou Diawara

Abstract


In this paper, fundamental results of the joint distribution of the bivariate exponential distributions are established.  The positive support multivariate distribution theory is important in reliability and survival analysis, and we applied it to the case where more than one failure or survival is observed in a given study. Usually, the multivariate distribution is restricted to those with marginal distributions of a specified and familiar lifetime family. The family of exponential distribution contains the absolutely continuous and discrete case models with a nonzero probability on a set of measure zero. Examples are given, and estimators are developed and applied to simulated data. Our findings generalize substantially known results in the literature, provide flexible and novel approach for modeling related events that can occur simultaneously from one based event.


Keywords


Bivariate exponential; Dirac delta; Reliability models; Survival analysis

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DOI: http://dx.doi.org/10.18187/pjsor.v6i1.143

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Title

THE JOINT DISTRIBUTION OF BIVARIATE EXPONENTIAL UNDER LINEARLY RELATED MODEL

Keywords

Bivariate exponential; Dirac delta; Reliability models; Survival analysis

Description

In this paper, fundamental results of the joint distribution of the bivariate exponential distributions are established.  The positive support multivariate distribution theory is important in reliability and survival analysis, and we applied it to the case where more than one failure or survival is observed in a given study. Usually, the multivariate distribution is restricted to those with marginal distributions of a specified and familiar lifetime family. The family of exponential distribution contains the absolutely continuous and discrete case models with a nonzero probability on a set of measure zero. Examples are given, and estimators are developed and applied to simulated data. Our findings generalize substantially known results in the literature, provide flexible and novel approach for modeling related events that can occur simultaneously from one based event.


Date

2010-09-21

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol 6. No. 1, Jan 2010



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810