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Handling Outliers and Missing Data in Regression Models Using R: Simulation Examples

Author

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  • Mohamed Reda Abonazel

    (Department of Applied Statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt)

Abstract

This paper has reviewed two important problems in regression analysis (outliers and missing data), as well as some handling methods for these problems. Moreover, two applications have been introduced to understand and study these methods by R-codes. Practical evidence was provided to researchers to deal with those problems in regression modeling with R. Finally, we created a Monte Carlo simulation study to compare different handling methods of missing data in the regression model. Simulation results indicate that, under our simulation factors, the k-nearest neighbors method is the best method to estimate the missing values in regression models.

Suggested Citation

  • Mohamed Reda Abonazel, 2020. "Handling Outliers and Missing Data in Regression Models Using R: Simulation Examples," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 6(8), pages 187-203, 10-2020.
    • Handle: RePEc:arp:ajoams:2020:p:187-203
    DOI: 10.32861/ajams.68.187.203
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    References listed on IDEAS

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    1. Abonazel, Mohamed R., 2015. "How to Create a Monte Carlo Simulation Study using R: with Applications on Econometric Models," MPRA Paper 68708, University Library of Munich, Germany.
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