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

Sergio Arciniegas-Alarcón; Marisol García-Peña; Wojtek Janusz Krzanowski; Carlos Tadeu dos Santos Dias

Biometrical Letters (2014)

  • Volume: 51, Issue: 2, page 75-88
  • ISSN: 1896-3811

Abstract

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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.

How to cite

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Sergio Arciniegas-Alarcón, et al. "An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects." Biometrical Letters 51.2 (2014): 75-88. <http://eudml.org/doc/268748>.

@article{SergioArciniegas2014,
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.},
author = {Sergio Arciniegas-Alarcón, Marisol García-Peña, Wojtek Janusz Krzanowski, Carlos Tadeu dos Santos Dias},
journal = {Biometrical Letters},
keywords = {cross-validation; singular value decomposition; imputation; genotype-by-environment interaction; weights; missing values},
language = {eng},
number = {2},
pages = {75-88},
title = {An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects},
url = {http://eudml.org/doc/268748},
volume = {51},
year = {2014},
}

TY - JOUR
AU - Sergio Arciniegas-Alarcón
AU - Marisol García-Peña
AU - Wojtek Janusz Krzanowski
AU - Carlos Tadeu dos Santos Dias
TI - An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects
JO - Biometrical Letters
PY - 2014
VL - 51
IS - 2
SP - 75
EP - 88
AB - 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.
LA - eng
KW - cross-validation; singular value decomposition; imputation; genotype-by-environment interaction; weights; missing values
UR - http://eudml.org/doc/268748
ER -

References

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