Analysis of Incomplete Data Under Different Missingness Mechanism using Imputation Methods for Wheat Genotypes

Abstract

Missing values is a persistent problem in analysis of agriculture data. To improve the quality of the data in the agriculture study, imputation has drawn a lot of research interest. Non-missing data was removed with varying frequency from the genotypic data of the wheat crop by different missingness mechanism. Imputation methods namely last observation carried forward, mean, regression and KNN are applied to these data sets and compared their parameter with the parameter of original data. The performances of imputation methods are also evaluated by root mean square error for solving missing values at different missingness mechanism.