Imputaciones múltiples, herramienta para la estimación de datos faltantes en la modelación de regresión

Multiple Imputations, tool for the estimation of missing data in regression modeling

En los últimos años se ha apreciado un incremento en la investigación sobre problemas de datos faltantes, siendo la imputación múltiple una fundamental alternativa; donde los conjuntos de datos a menudo presentan complejidades que son actualmente difíciles de manejar de manera apropiada en el marco de probabilidad, pero relativamente simples de tratar con imputación; por esto, el presente artículo describe una serie de aspectos prácticos para aplicar dicha metodología en el caso de la modelación de captura de carbono para Colombia, con base en las bases de datos del Banco Mundial incluyendo datos faltantes alcanzando R2 de 79,30%, resaltándose que al momento de estimar dichos datos y recalcular el modelo respectivo se evidencia un mayor R2, siendo del 94,79%, lo cual evidencia una mejora sustancial del respectivo modelo de regresión lineal múltiple como tal.

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