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An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects Cover

An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects

Open Access
|Dec 2014

Abstract

A common problem in multi-environment trials arises when some genotypeby- environment combinations are missing. In Arciniegas-Alarcón et al. (2010) we outlined a method of data imputation to estimate the missing values, the computational algorithm for which was a mixture of regression and lower-rank approximation of a matrix based on its singular value decomposition (SVD). In the present paper we provide two extensions to this methodology, by including weights chosen by cross-validation and allowing multiple as well as simple imputation. The three methods are assessed and compared in a simulation study, using a complete set of real data in which values are deleted randomly at different rates. The quality of the imputations is evaluated using three measures: the Procrustes statistic, the squared correlation between matrices and the normalised root mean squared error between these estimates and the true observed values. None of the methods makes any distributional or structural assumptions, and all of them can be used for any pattern or mechanism of the missing values.

DOI: https://doi.org/10.2478/bile-2014-0006 | Journal eISSN: 2199-577X | Journal ISSN: 1896-3811
Language: English
Page range: 75 - 88
Published on: Dec 20, 2014
Published by: Polish Biometric Society
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2014 Sergio Arciniegas-Alarcón, Marisol García-Peña, Wojtek Janusz Krzanowski, Carlos Tadeu dos Santos Dias, published by Polish Biometric Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.