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

Confirmation, Correction and Improvement for Outlier Validation using Dummy Variables

Arzdar KİRACİ
Sayfa
43–52
Yayın Tarihi
2013-09-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Dummy variables can be used to detect, validate and measure the impact of outliers in data.
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Temel Bulgular & Veri:This paper uses a model to evaluate the effectiveness of dummy variables in detecting outliers.
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Sonuç & Katkı:We recommend all previous studies using this statistics be revised in light of the findings presented in this paper.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler: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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Bu Makaleye Atıf Yap

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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Kaynakça (3)

  1. Billingsley, P., D.A. Belsley, E. Kuh and R.E. Welsch (1980). Regression diagnostics: identifying influential data and sources of collinearity. Wiley Series in Probability and Mathematical Statistics. New York-Chichester-Brisbane: Wiley. MR0576408 Cook, R.D. (1985). Detection of influential observation in linear regression. Technometrics, 19, 15–18. MR0436478 Frees, E.W. (2004). Longitudinal and Panel Data: Analysis and Applications in the Social Sciences. Cambridge: Cambridge University Press. MR2087947 Greene, W. H. (2002).
  2. Econometric Analysis. Fifth edition, NJ: Prentice Hall. Rousseeuw, P.J. and A.M. Leroy (1987). Robust regression and outlier detection. New York: John Wiley & Sons, Inc. MR0914792 Rousseeuw, P.J. and S. Van Aelst (1999). Positive-breakdown robust methods in computer vision. Computing Science and Statistics, 31, 451-460.
  3. Studenmund, A.H. (2002). Using Econometrics: A Practical Guide. London: Addison Wesley. Timm, N.H. (2002). Applied Multivariate Analysis. New York: Springer-Verlag. MR1908225 Zaman, A., P.J. Rousseeuw and M. Orhan (2001). Econometric applications of high- breakdown robust regression techniques. Economics Letters, 71 (1), 1-8.