JSM 2005 - Toronto

Abstract #304246

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Activity Number: 451
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #304246
Title: A General Class of Linearly-structured Bivariate Lifetime Distributions
Author(s): Norou Diawara*+ and Mark Carpenter and Yi Han
Companies: Auburn University and Auburn University and Auburn University
Address: 1402 Hampton Drive, Auburn, AL, 36830, United States
Keywords: Dirac Delta ; Bivariate Exponential ; Weibull ; Laplace Transform ; Gamma ; Mixtures
Abstract:

In this paper, we present a method for deriving general classes of bivariate lifetime distributions. A method for creating linear associated bivariate pairs is presented that, when a solution exists, guarantees preselected marginal distributions, including exponential, Gamma, and extreme-value distributions. We further develop a general class of finite bivariate mixtures, examine its mathematical properties, and discuss its application to supervised and unsupervised classification. Bivariate parameter estimation is developed and applied to real and simulated data. Additionally, an internet traffic example is provided where the data are assumed to be bivariate finite mixtures of connection (arrival) and download (service) time-pairs.

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