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:: Volume 18, Issue 2 (2-2025) ::
JSS 2025, 18(2): 0-0 Back to browse issues page
Comparison of Neyman-Pearson and Evidential Approaches in Separate One-Sided Tests Based on Model Misspecification Error
Ali Dastbaravarde *
Abstract:   (709 Views)
In statistical hypothesis testing, model misspecification error occurs when the real model of the data is none of the models under null and alternative hypotheses. This research has studied the probability of model misspecification errors in one-sided tests. These error rates are compared between the Neyman-Pearson and evidential statistical inference approaches. The results show that the evidential approach works better than the Neyman-Pearson approach.
Keywords: Evidential inference, Frequentist inference, Model misspecification, Composite hypothesis.
Full-Text [PDF 340 kb]   (360 Downloads)    
Type of Study: Research | Subject: Theoritical Statistics
Received: 2024/03/28 | Accepted: 2024/05/30 | Published: 2024/12/2
References
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Dastbaravarde A. Comparison of Neyman-Pearson and Evidential Approaches in Separate One-Sided Tests Based on Model Misspecification Error. JSS 2025; 18 (2)
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 18, Issue 2 (2-2025) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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