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Journals / Hacettepe Journal of Mathematics and Statistics / 2012 / Cilt: 41 - Sayı: 1

Outlier detection by regression diagnostics based on robust parameter estimates

Semra TÜRKAN
Meral Candan ÇETİN
Öniz TOKTAMIŞ
Pages
147–155
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

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

A simulation study is per- formed to compare the performance of the classical diagnostics with the proposed diagnostics based on robust M estimation to identify outliers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this article, robust versions of some of the frequently used diagnos- tics are considered to identify outliers instead of the diagnostics based on the least square method.
📊
Key Findings & Data:These diagnostics are Cook’s distance, the Welsch-Kuh distance and the Hadi measure.
💡
Conclusion & Impact:A simulation study is per- formed to compare the performance of the classical diagnostics with the proposed diagnostics based on robust M estimation to identify outliers.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this article, robust versions of some of the frequently used diagnos- tics are considered to identify outliers instead of the diagnostics based on the least square method. These diagnostics are Cook’s distance, the Welsch-Kuh distance and the Hadi measure. A simulation study is per- formed to compare the performance of the classical diagnostics with the proposed diagnostics based on robust M estimation to identify outliers.

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Cite This Article

TÜRKAN, S., ÇETİN, M. C., TOKTAMIŞ, Ö. (2022). Outlier detection by regression diagnostics based on robust parameter estimates. Hacettepe Journal of Mathematics and Statistics, Cilt: 41 - Sayı: 1, 147–155. https://ajindex.com/articles/530456

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