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Research Article Open access CC BY 4.0

Chris-Jerry Distribution and Its Applications

Chrisogonus K. Onyekwere, Okechukwu J. Obulezi

Asian Journal of Probability and Statistics · pp. 16–30 · Published 17 Sep 2022

10.9734/ajpas/2022/v20i130480

Abstract

In this paper, a new one-parameter distribution named Chris-Jerry is suggested from two component mixture of Exponential (\(\theta\)) distribution and Gamma(3; \(\theta\)) distribution with mixing proportion \(p=\frac{\theta}{\theta + 2}\) having a flexibility advantage in modeling lifetime data. The statistical properties are discussed and the maximum likelihood estimation procedure is used to obtain the parameter estimate. The Convolution of the product of Pareto random variable with the proposed Chris-Jerry distributed random variable is explored with its marginal density derived. To illustrate the usefulness, three sets of lifetime data are employed and LL, AIC, BIC and K-S statistics are obtained for Exponential, Ishita, Akash, Rama, Pranav, Rani, Lindley, Sujatha, Aradhana, Shanker and XGamma and the Chris-Jerry distributions.

Chris-Jerry distribution exponential distribution gamma distribution component mixture heavy-tailed distribution

References (16)

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