Missing value imputation using least squares techniques in contaminated matrices.

ABSTRACT: This paper describes strategies to reduce the possible effect of outliers on the quality of imputations produced by a method that uses a mixture of two least squares techniques: regression and lower rank approximation of a matrix. To avoid the influence of discrepant data and maintain the computational speed of the original scheme, pre-processing options were explored before applying the imputation method. The first proposal is to previously use a robust singular value decomposition, the second is to detect outliers and then treat the potential outliers as missing. To evaluate the proposed methods, a cross-validation study was carried out on ten complete matrices of real data from multi-environment trials. The imputations were compared with the original data using three statistics: a measu

SUBMITTER: Garcia-Pena M 

PROVIDER: S-EPMC9036115 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature