EconPapers    
Economics at your fingertips  
 

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

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.

Keywords: Missing data; Monte carlo simulation; Multiple imputation methods; R-software; Robust regression estimators. (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.arpgweb.com/pdf-files/ajams6(8)187-203.pdf (application/pdf)
https://www.arpgweb.com/journal/17/archive/10-2020/8/6 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arp:ajoams:2020:p:187-203

Access Statistics for this article

Academic Journal of Applied Mathematical Sciences is currently edited by Dr. Diana Bílková

More articles in Academic Journal of Applied Mathematical Sciences from Academic Research Publishing Group Rahim Yar Khan 64200, Punjab, Pakistan.
Bibliographic data for series maintained by Managing Editor ().

 
Page updated 2021-05-19
Handle: RePEc:arp:ajoams:2020:p:187-203