We provide illustrative examples with real and simulated data sets to demonstrate the applicability of the proposed Mix_BW model.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, we propose a mixture model containing bivariate Weibull distributions—the Marshall-Olkin bivariate Weibull (MOBW) and the Block-Basu bivariate Weibull (BBBW)—because each of these distributions alone is inadequate for explaining a data set when certain special situations occur in bivariate lifetime data sets.
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Key Findings & Data:We refer to the proposed model as Mix_BW.
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Conclusion & Impact:We provide illustrative examples with real and simulated data sets to demonstrate the applicability of the proposed Mix_BW model.
In this paper, we propose a mixture model containing bivariate Weibull distributions—the Marshall-Olkin bivariate Weibull (MOBW) and the Block-Basu bivariate Weibull (BBBW)—because each of these distributions alone is inadequate for explaining a data set when certain special situations occur in bivariate lifetime data sets. We refer to the proposed model as Mix_BW. To estimate the model parameters, we use the expectation-maximisation (EM) algorithm in an adapted form we term the Mix_EM algorithm. We provide illustrative examples with real and simulated data sets to demonstrate the applicability of the proposed Mix_BW model.
TÜRKAN, A. H., ÇALIŞ, N. (2018). A Mixture Model of Two Bivariate Weibull Distributions. Gazi University Journal of Science, Cilt: 31 - Sayı: 2, 643–658. https://ajindex.com/articles/254475