INFORMAZIONI SU QUESTO ARTICOLO

Cita

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/S0103-90162008000400015Search 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.2009.0014Search 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.2010.03352420681483Search 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_20Search 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/cropsci2003.8650Search 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/cropsci2013.04.0241Search in Google Scholar

Gauch H.G., Zobel R.W. (1990): Imputing missing yield trial data. Theoretical and Applied Genetics 79: 753-761.10.1007/BF0022424024226735Search 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/s11634-011-0086-7Search 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.10.1002/9780470238004Search 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.2012.03.007Search in Google Scholar

Little R., Rubin D. (2002): Statistical analysis with missing data. 2nd ed. John Wiley & Sons, New York, NY. 10.1002/9781119013563Search 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/cropsci2005.04-0038Search 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/S0103-90162011000600012Search 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/GENSR1202325SSearch 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.7.2.147Search in Google Scholar

van Buuren S. (2012): Flexible imputation of missing data. CRC press.10.1201/b11826Search 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/cropsci2011.01.0016Search in Google Scholar

Yan W. (2013): Biplot analysis of incomplete two-way data. Crop Science 53(1): 48-57. 10.2135/cropsci2012.05.0301Search in Google Scholar

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