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Journals / Hacettepe Journal of Mathematics and Statistics / 2019 / Cilt: 48 - Sayı: 2

DE- and EDPM- compound optimality for the information and probability-based criteria

W. A. Hassanein
N. M. Kilany
Pages
580–591
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

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

Finally, two numerical examples are given to illustrate the potentiality of the proposed compound criteria.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Several optimality criteria have been considered in the literature as information-based criteria.
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Key Findings & Data:The probability- based criteria have been recently proposed for maximizing the probability of a desired outcome.
💡
Conclusion & Impact:Finally, two numerical examples are given to illustrate the potentiality of the proposed compound criteria.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Several optimality criteria have been considered in the literature as information-based criteria. The probability- based criteria have been recently proposed for maximizing the probability of a desired outcome. However, designs that are optimal for the information- based criteria may be inadequate for probability- based criteria. This paper introduces the DE- and EDPM – optimum designs for multi aims of optimalityforGeneralizedLinearModels(GLMs). Anequivalencetheorem is proved for both compound criteria. Finally, two numerical examples are given to illustrate the potentiality of the proposed compound criteria.

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Cite This Article

Hassanein, W. A., Kilany, N. M. (2022). DE- and EDPM- compound optimality for the information and probability-based criteria. Hacettepe Journal of Mathematics and Statistics, Cilt: 48 - Sayı: 2, 580–591. https://ajindex.com/articles/529743

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