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Journals / Hacettepe Journal of Mathematics and Statistics / 2017 / Cilt: 46 - Sayı: 6

Bivariate Weibull-power series class of distributions

Saralees NADARAJAH
Rasool ROOZEGAR
Pages
1175–1186
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We propose a more general class of distributions and illustrate its use with two real data sets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We point out that some of the results in Kundu and Gupta [3] in otherwise an excellent paper are incorrect.
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Key Findings & Data:We point out that some of the results in Kundu and Gupta [3] in otherwise an excellent paper are incorrect.
💡
Conclusion & Impact:We propose a more general class of distributions and illustrate its use with two real data sets.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:EM algorithm · Maximum likelihood estimation · Power series distribution

Abstract

We point out that some of the results in Kundu and Gupta [3] in otherwise an excellent paper are incorrect. We propose a more general class of distributions and illustrate its use with two real data sets.

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Cite This Article

NADARAJAH, S., ROOZEGAR, R. (2022). Bivariate Weibull-power series class of distributions. Hacettepe Journal of Mathematics and Statistics, Cilt: 46 - Sayı: 6, 1175–1186. https://ajindex.com/articles/530015

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References (6)

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  2. Crowder, M.J., Kimber, A., Sweeting, T. and Smith, R. Statistical Analysis of Reliability Data, Chapman and Hall, London, 1994.
  3. Kundu, D. and Gupta, A.K. On bivariate Weibull-geometric distribution, Journal of Multivariate Analysis 123, 19-29, 2014.
  4. Marshall, A.W. and Olkin, I. A multivariate exponential distribution, Journal of the American Statistical Association 62, 30-44, 1967.
  5. Marshall, A.W. and Olkin, I. A new method of adding a parameter to a family of distributions with application to the exponential and Weibull families, Biometrika 84, 641-652, 1997. R Development Core Team. A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Vienna, Austria, 2015.
  6. Schwarz, G.E. Estimating the dimension of a model, Annals of Statistics 6, 461-464, 1978.