Handling Outliers and Missing Data in Regression Models Using R: Simulation Examples
Mohamed Abonazel ()
Academic Journal of Applied Mathematical Sciences, 2020, vol. 6, issue 8, 187-203
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.
Keywords: Missing data; Monte carlo simulation; Multiple imputation methods; R-software; Robust regression estimators. (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:arp:ajoams:2020:p:187-203
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