JOURNAL ARTICLE

Weighted Marshall–Olkin bivariate exponential distribution

Ahad JamalizadehDebasis Kundu

Year: 2012 Journal:   Statistics Vol: 47 (5)Pages: 917-928   Publisher: Taylor & Francis

Abstract

Abstract Recently, Gupta and Kundu [R.D. Gupta and D. Kundu, A new class of weighted exponential distributions, Statistics 43 (2009), pp. 621–634] have introduced a new class of weighted exponential (WE) distributions, and this can be used quite effectively to model lifetime data. In this paper, we introduce a new class of weighted Marshall–Olkin bivariate exponential distributions. This new singular distribution has univariate WE marginals. We study different properties of the proposed model. There are four parameters in this model and the maximum-likelihood estimators (MLEs) of the unknown parameters cannot be obtained in explicit forms. We need to solve a four-dimensional optimization problem to compute the MLEs. One data set has been analysed for illustrative purposes and finally we propose some generalization of the proposed model. Keywords: joint probability density functionconditional probability density functionsingular distributionmaximum-likelihood estimatorsFisher information matrixasymptotic distribution Acknowledgements The authors thank the two referees for their valuable suggestions which has helped them to improve the manuscript significantly. Part of this work has been supported by a grant from the Department of Science and Technology, Government of India. Additional informationNotes on contributorsDebasis Kundu Visiting professor at the King Saud University, Riyadh, Saudi Arabia.

Keywords:
Mathematics Bivariate analysis Estimator Applied mathematics Statistics Univariate Exponential distribution Class (philosophy) Generalization Exponential function Joint probability distribution Exponential family Multivariate statistics Mathematical analysis Computer science Artificial intelligence

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Citation History

Topics

Statistical Distribution Estimation and Applications
Physical Sciences →  Mathematics →  Statistics and Probability
Reliability and Maintenance Optimization
Physical Sciences →  Engineering →  Safety, Risk, Reliability and Quality
Probabilistic and Robust Engineering Design
Social Sciences →  Decision Sciences →  Statistics, Probability and Uncertainty

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