Multiple Imputations, tool for the estimation of missing data in regression modeling
2019
Mejía-Giraldo, Luis Miguel | Restrepo-Betancur, Luis Fernando
In recent years there has been an increase in research on missing data problems, with multiple imputation being a fundamental alternative; where data sets often present complexities that are currently difficult to manage appropriately in the probability framework, but relatively simple to deal with imputation; For this reason, this article describes a series of practical aspects to apply this methodology in the case of carbon capture modeling for Colombia, based on the World Bank databases including missing data reaching R2 of 79.2988%, highlighting that when estimating said data and recalculating the respective model, a greater R2 is evidenced, being of 94.76901%, which evidences a substantial improvement of the respective multiple linear regression model as such.
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