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Modeling Discrete Bivariate Data with Applications to Failure and Count Data
Author(s) -
Lee Hyunju,
Cha Ji Hwan,
Pulcini Gianpaolo
Publication year - 2017
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2118
Subject(s) - bivariate analysis , joint probability distribution , bivariate data , mathematics , count data , poisson distribution , class (philosophy) , random variable , conditional probability distribution , statistics , econometrics , computer science , artificial intelligence
In this study, we propose a new class of flexible bivariate distributions for discrete random variables. The proposed class of distribution is based on the notion of conditional failure rate for a discrete‐type random variable. We derive general formulae for the joint distributions belonging to the proposed class that, unlike other discrete bivariate models already proposed in the literature such as the well‐known and most popular Holgate's bivariate Poisson distribution, can model both positive and negative dependence . We discuss general statistical properties of the proposed class as well. Specific families of bivariate distributions can be generated from the general class proposed in this paper just by specifying the ‘baseline distributions’. Furthermore, specific discrete bivariate distributions belonging to the proposed class are applied to analyze three real data sets, and the results are compared with those obtained from conventional models. Copyright © 2017 John Wiley & Sons, Ltd.