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Journals / Gazi University Journal of Science / 2026 / Volume: 24 Issue: 1

PURO: A Package for Unmasking Regression Outliers

Enis Sınıksaran
Mehmet Satman
Pages
—
Publication Date
2011-01-14
DOI
—
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AI Summary & Key Takeaways

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

A Mathematica package PURO is introduced which implements seven methods from the latest and most respected outlier detection procedures in the statistics literature.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Multiple regression outliers should be identified because of their potential effect on parameter estimates and inferences from the regression model.
📊
Key Findings & Data:In recent years, researchers have proposed numerous strategies and procedures to identify the outliers.
💡
Conclusion & Impact:A Mathematica package PURO is introduced which implements seven methods from the latest and most respected outlier detection procedures in the statistics literature.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Regression outlier · Mathematica · Detection procedure

Abstract

Multiple regression outliers should be identified because of their potential effect on parameter estimates and inferences from the regression model. In recent years, researchers have proposed numerous strategies and procedures to identify the outliers. A Mathematica package PURO is introduced which implements seven methods from the latest and most respected outlier detection procedures in the statistics literature.

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Cite This Article

Sınıksaran, E., Satman, M. (2011). PURO: A Package for Unmasking Regression Outliers. Gazi University Journal of Science, Volume: 24 Issue: 1. https://ajindex.com/articles/1269935

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