1Department of Mathematics and Statistics, College of Basic Science and Humanities, CCS Haryana Agricultural University, Hisar-125 004, Haryana, India
2School of Computer Applications, Lovely Professional University, Phagwara-144 411, Punjab, India
*Corresponding Author: Sanju, Department of Mathematics and Statistics, College of Basic Science and Humanities, CCS Haryana Agricultural University, Hisar-125 004, Haryana, India, Email: sanjukularia111@gmail.com
Online published on 6 November, 2023.
The issue of missing data is prevalent in all type of research work, which can diminish statistical power and lead to inaccurate results if not managed correctly. Missing data cannot be ignored because every piece of data, no matter how small, affects the outcome significantly. Imputation is a key component in dealing with missing data.
Our goal of this paper is to compare four more recently developed imputation techniques-MICE, MI, miss forest and Amelia. In order to examine the performance of various imputation techniques, non-missing data were deleted from genotypic data of soybean crop with varied frequency of missingness by missing completely at random mechanism. The study compared different imputation techniques for solving missing values using the root mean square error and mean absolute error.
To fill in the dataset's missing values, the imputation technique producing the lowest value of the RMSE and MAE will be taken into consideration. Finally, it is observed that missForest technique performs best on the genotypic data of soybean at different proportion of missingness.
Amelia, Missing completely at random, Missing data, Multiple imputation by chained equation
