REGULAR ARTICLE
Multiple imputation procedures using the GabrielEigen algorithm
Marisol García-Peña, Sergio Arciniegas-Alarcón, Wojtek Krzanowski,
Décio Barbin
Commun. Biometry Crop Sci. (2016) 11 (2), 149-163.
ABSTRACT
GabrielEigen is a simple deterministic imputation system without structural or distributional assumptions,
which uses a mixture of regression and lower-rank approximation of a matrix based on its singular
value decomposition. We provide multiple imputation alternatives (MI) based on this system, by adding
random quantities and generating approximate confidence intervals with different widths to the imputations
using cross-validation (CV). These methods are assessed by a simulation study using real data matrices in
which values are deleted randomly at different rates, and also in a case where the missing observations have
a systematic pattern. The quality of the imputations is evaluated by combining the variance between imputations
(Vb) and their mean squared deviations from the deleted values (B) into an overall measure (Tacc). It is shown
that the best performance occurs when the interval width matches the imputation error associated with GabrielEigen.
Key Words: imputation; missing values; singular value decomposition; cross-validation; unbalanced.
