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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Finally, these estimate methods are applied to a real data set based on patients with breast cancer in order to demonstrate the applicabilities.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, we estimate the parameters of the Marshall-Olkin generalized exponential distribution under progressive Type-I interval censoring based on maximum likelihood, moment method and probability plot.
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Key Findings & Data:A simulation study is conducted to compare these estimates in terms of mean squared errors and biases.
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Conclusion & Impact:Finally, these estimate methods are applied to a real data set based on patients with breast cancer in order to demonstrate the applicabilities.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:EM algorithm · Generalized exponential distribution · Maximum likelihood estimate
Abstract
In this paper, we estimate the parameters of the Marshall-Olkin generalized exponential distribution under progressive Type-I interval censoring based on maximum likelihood, moment method and probability plot. A simulation study is conducted to compare these estimates in terms of mean squared errors and biases. Finally, these estimate methods are applied to a real data set based on patients with breast cancer in order to demonstrate the applicabilities.
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Cite This Article
Torabi, H., Forooghi, M., Nadeb, H. (2016). Comparing of some estimation methods for parameters of the Marshall-Olkin generalized exponential distribution under progressive Type-I interval censoring. Hacettepe Journal of Mathematics and Statistics, Volume: 45 Issue: 5. https://ajindex.com/articles/1403111