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

DE- and EDP$_{M}$- compound optimality for the information and probability-based criteria

W. A. HASSANEİN
N. M. KİLANY
Pages
580–591
Publication Date
2019-04-01
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.
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Conclusion & Impact:Finally, two numerical examples are given to illustrate the potentiality of the proposed compound criteria.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Optimum design · E-optimality · D-optimality

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 EDP${}_{M}$ -- optimum designs for multi aims of optimality for Generalized Linear Models (GLMs). An equivalence theorem 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

HASSANEİN, W. A., KİLANY, N. M. (2019). DE- and EDP$_{M}$- 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/669390

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