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Journals / International Econometric Review / 2013 / Cilt: 5 - Sayı: 2

Confirmation, Correction and Improvement for Outlier Validation using Dummy Variables

Arzdar KİRACİ
Pages
43–52
Publication Date
2013-09-01
DOI
—
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AI Summary & Key Takeaways

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

We recommend all previous studies using this statistics be revised in light of the findings presented in this paper.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Dummy variables can be used to detect, validate and measure the impact of outliers in data.
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Key Findings & Data:This paper uses a model to evaluate the effectiveness of dummy variables in detecting outliers.
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Conclusion & Impact:We recommend all previous studies using this statistics be revised in light of the findings presented in this paper.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Dummy Variable · t-Statistic · Outlier

Abstract

Dummy variables can be used to detect, validate and measure the impact of outliers in data. This paper uses a model to evaluate the effectiveness of dummy variables in detecting outliers. While generally confirming some findings in the literature, the model refutes the presumption that the t˗statistic or the F-incremental statistic is enough to validate an observation as an outlier. In order to rectify this fallacy, this paper recommends an easily-calculable robust standardized residual statistic that is more compatible with the definition of outliers. The robust standardized residual statistic suggested herein is still used in many robust regression methods and is more effective than the t-statistic or the F-incremental statistic in validating outliers with dummy variables. The results of this study suggest some practical recommendations for dealing with outliers and improvements in maintaining the integrity of data. We recommend all previous studies using this statistics be revised in light of the findings presented in this paper.

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Cite This Article

KİRACİ, A. (2013). Confirmation, Correction and Improvement for Outlier Validation using Dummy Variables. International Econometric Review, Cilt: 5 - Sayı: 2, 43–52. https://ajindex.com/articles/832161

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References (3)

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