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Journals / Hacettepe Journal of Mathematics and Statistics / 2020 / Cilt: 49 - Sayı: 6

An estimation of Phi divergence and its application in testing normality

Mahsa Tavakoli
Hadi Alizadeh Noughabi
Gholam Reza Mohtashami Borzadaran
Pages
2104–2118
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

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

It is shown that each special case of Phi divergence based test is the most powerful in each group of alternatives (depending on symmetry or support).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this article, a new goodness of fit test for normality is introduced based on Phi divergence.
📊
Key Findings & Data:The test statistic is estimated using spacing and the consistency of the test is proved.
💡
Conclusion & Impact:It is shown that each special case of Phi divergence based test is the most powerful in each group of alternatives (depending on symmetry or support).
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Phi divergence measure · Normality test · Spacing

Abstract

In this article, a new goodness of fit test for normality is introduced based on Phi divergence. The test statistic is estimated using spacing and the consistency of the test is proved. Then with replacing some special cases of Phi divergence, the efficiency of each test statistic is analyzed by Monte Carlo simulation against some competitors (based on Phi divergence using kernel density function and also some classical competitors). It is shown that each special case of Phi divergence based test is the most powerful in each group of alternatives (depending on symmetry or support).

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Cite This Article

Tavakoli, M., Noughabi, H. A., Borzadaran, G. R. M. (2022). An estimation of Phi divergence and its application in testing normality. Hacettepe Journal of Mathematics and Statistics, Cilt: 49 - Sayı: 6, 2104–2118. https://ajindex.com/articles/530047

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