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Journals / Gazi University Journal of Science / 2016 / Cilt: 29 - Sayı: 4

IMPUTATION AND DELETION METHODS UNDER THE PRESENCE OF MISSING VALUES AND OUTLIERS: A COMPARATIVE STUDY

Onur Toka
Meral Çetin
Pages
799–809
Publication Date
2016-12-19
DOI
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AI Summary & Key Takeaways

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

We compare the imputed values with real values and explain the results of classical and robust imputation methods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Missing data and imputation methods are studied in many disciplines.
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Key Findings & Data:However, the methods have some different properties and some constraints according to missingness mechanism.
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Conclusion & Impact:We compare the imputed values with real values and explain the results of classical and robust imputation methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:ER Algorithm · Missing data · Outliers

Abstract

Missing data and imputation methods are studied in many disciplines. However, the methods have some different properties and some constraints according to missingness mechanism. In this paper, we examine some deletion and imputation methods’ behaviors under the presence of outliers. We obtain a mean vector and covariance matrix with missing and contaminated data and compare the results of imputation methods using mean square errors. In second application, we use the regression data and examine the effect of missingness on regression model’s parameters. We compare the imputed values with real values and explain the results of classical and robust imputation methods.

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Cite This Article

Toka, O., Çetin, M. (2016). IMPUTATION AND DELETION METHODS UNDER THE PRESENCE OF MISSING VALUES AND OUTLIERS: A COMPARATIVE STUDY. Gazi University Journal of Science, Cilt: 29 - Sayı: 4, 799–809. https://ajindex.com/articles/254601

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