[Arciniegas-Alarcón S., García-Peña M., Dias C.T.S. (2011): Data imputation in trials with genotype×environment interaction. Interciencia 36(6): 444-449.]Search in Google Scholar
[Arciniegas-Alarcón S., García-Peña M., Dias C.T.S., Krzanowski W.J. (2010): An alternative methodology for imputing missing data in trials with genotypeby- environment interaction. Biometrical Letters 47(1): 1-14.]Search in Google Scholar
[Bergamo G.C., Dias C.T.S., Krzanowski W.J. (2008): Distribution-free multiple imputation in an interaction matrix through singular value decomposition. Scientia Agricola 65(4): 422-427.10.1590/S0103-90162008000400015]Search in Google Scholar
[Calinski T., Czajka S., Kaczmarek Z., Krajewski P., Pilarczyk W. (2009): Analyzing the Genotype-by-Environment Interactions Under a Randomization- Derived Mixed Model. Journal of Agricultural, Biological and Environmental Statistics 14(2): 224-241.10.1198/jabes.2009.0014]Search in Google Scholar
[Ching W., Li L., Tsing N., Tai C., Ng T. (2010): A weighted local least squares imputation method for missing value estimation in microarray gene expression data. International Journal of Data Mining and Bioinformatics 4(3): 331-347.10.1504/IJDMB.2010.03352420681483]Search in Google Scholar
[Denis J.B., Baril C.P. (1992): Sophisticated models with numerous missing values: the multiplicative interaction model as an example. Biuletyn Oceny Odmian 24-25: 33-45.]Search in Google Scholar
[Di Ciaccio A. (2011): Bootstrap and nonparametric predictors to impute missing data. In: B. Fichet et al. (eds.), Classification and Multivariate Analysis for Complex Data Structures, Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag Berlin Heidelberg.10.1007/978-3-642-13312-1_20]Search in Google Scholar
[Dias C.T.S., Krzanowski W.J. (2003): Model selection and cross validation in additive main effect and multiplicative interaction models. Crop Science 43: 865-873.10.2135/cropsci2003.8650]Search in Google Scholar
[Gabriel K.R. (2002): Le biplot - outil d’exploration de données multidimensionelles. Journal de la Société Française de Statistique 143(3-4): 5-55.]Search in Google Scholar
[García-Peña M., Dias C.T.S. (2009): Analysis of bivariate additive models with multiplicative interaction (AMMI). Biometric Brazilian Journal 27(4): 586-602.]Search in Google Scholar
[Gauch H.G. (2013): A simple protocol for AMMI analysis of yield trials. Crop Science 53: 1860-1869.10.2135/cropsci2013.04.0241]Search in Google Scholar
[Gauch H.G., Zobel R.W. (1990): Imputing missing yield trial data. Theoretical and Applied Genetics 79: 753-761.10.1007/BF0022424024226735]Search in Google Scholar
[Josse J., Pagès J., Husson F. (2011): Multiple imputation in PCA. Advances in data analysis and classification 5(3): 231-246.10.1007/s11634-011-0086-7]Search in Google Scholar
[Josse J., Husson F. (2012): Handling missing values in exploratory multivariate data analysis methods. Journal de la Société Française de Statistique 153(2): 79-99.]Search in Google Scholar
[Krzanowski W.J. (1988): Missing value imputation in multivariate data using the singular value decomposition of a matrix. Biometrical Letters XXV(1-2): 31-39.]Search in Google Scholar
[Krzanowski W.J. (2000): Principles of multivariate analysis: A user’s perspective. Oxford: University Press.]Search in Google Scholar
[Kroonenberg P.M. (2008): Applied multiway data analysis. John Wiley & Sons.10.1002/9780470238004]Search in Google Scholar
[Kumar A., Verulkar S.B., Mandal N.P., Variar M., Shukla V.D., Dwivedi J.L., Singh B.N., Singh O.N., Swain P., Mall A.K., Robin S., Chandrababu R., Jain A., Haefele S.M., Piepho H.P., Raman A. (2012): High-yielding, droughttolerant, stable rice genotypes for the shallow rainfed lowland droughtprone ecosystem. Field Crops Research 133: 37-47.10.1016/j.fcr.2012.03.007]Search in Google Scholar
[Little R., Rubin D. (2002): Statistical analysis with missing data. 2nd ed. John Wiley & Sons, New York, NY. 10.1002/9781119013563]Search in Google Scholar
[Paderewski J., Rodrigues P.C. (2014): The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data. Australian Journal of Crop Science 8: 640-645.]Search in Google Scholar
[Piepho H.P. (1995): Methods for estimating missing genotype-location combinations in multilocation trials - an empirical comparison. Informatik Biometrie und Epidemiologie in Medizin und Biologie 26: 335-349.]Search in Google Scholar
[Piepho H.P., Möhring J. (2006): Selection in cultivar trials - Is it ignorable? Crop Science 46: 192-201.10.2135/cropsci2005.04-0038]Search in Google Scholar
[R Development Core Team (2013): R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/ ]Search in Google Scholar
[Rodrigues P., Pereira D.G.S., Mexia J.T. (2011): A comparison between joint regression analysis and the additive main and multiplicative interaction model: the robustness with increasing amounts of missing data. Scientia Agricola 68(6): 679-686.10.1590/S0103-90162011000600012]Search in Google Scholar
[Rubin D.B. (1978): Multiple imputation in sample surveys: a phenomenological Bayesian approach to nonresponse. In: Survey Research Methods Section Of The American Statistical Association. Proceedings: 20-34.]Search in Google Scholar
[Sabaghnia N., Karimizadeh R., Mohammadi M. (2012): Model selection in additive main effect and multiplicative interaction model in durum wheat. Genetika 44(2): 325-339.10.2298/GENSR1202325S]Search in Google Scholar
[Schafer J.L., Graham J.W. (2002): Missing data: our view of the state of the art. Psychological Methods 7(2): 147-177.10.1037/1082-989X.7.2.147]Search in Google Scholar
[ van Buuren S. (2012): Flexible imputation of missing data. CRC press.10.1201/b11826]Search in Google Scholar
[Wright K. (2012): agridat: Agricultural datasets. R package version 1.4. http://CRAN.R-project.org/package=agridat>]Search in Google Scholar
[Yan W., Pageau D., Frégeau-Reid J., Durand J. (2011): Assessing the representativeness and repeatability of test locations for genotype evaluation. Crop Science 51: 1603-1610.10.2135/cropsci2011.01.0016]Search in Google Scholar
[Yan W. (2013): Biplot analysis of incomplete two-way data. Crop Science 53(1): 48-57. 10.2135/cropsci2012.05.0301]Search in Google Scholar