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

Open Access
|Dec 2014

References

  1. Arciniegas-Alarcón S., García-Peña M., Dias C.T.S. (2011): Data imputation in trials with genotype×environment interaction. Interciencia 36(6): 444-449.
  2. 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.
  3. 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
  4. 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
  5. 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
  6. 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.
  7. 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
  8. 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
  9. Gabriel K.R. (2002): Le biplot - outil d’exploration de données multidimensionelles. Journal de la Société Française de Statistique 143(3-4): 5-55.
  10. García-Peña M., Dias C.T.S. (2009): Analysis of bivariate additive models with multiplicative interaction (AMMI). Biometric Brazilian Journal 27(4): 586-602.
  11. Gauch H.G. (2013): A simple protocol for AMMI analysis of yield trials. Crop Science 53: 1860-1869.10.2135/cropsci2013.04.0241
  12. Gauch H.G., Zobel R.W. (1990): Imputing missing yield trial data. Theoretical and Applied Genetics 79: 753-761.10.1007/BF0022424024226735
  13. 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
  14. 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.
  15. 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.
  16. Krzanowski W.J. (2000): Principles of multivariate analysis: A user’s perspective. Oxford: University Press.
  17. Kroonenberg P.M. (2008): Applied multiway data analysis. John Wiley & Sons.10.1002/9780470238004
  18. 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
  19. Little R., Rubin D. (2002): Statistical analysis with missing data. 2nd ed. John Wiley & Sons, New York, NY. 10.1002/9781119013563
  20. 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.
  21. 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.
  22. Piepho H.P., Möhring J. (2006): Selection in cultivar trials - Is it ignorable? Crop Science 46: 192-201.10.2135/cropsci2005.04-0038
  23. 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/
  24. 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
  25. 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.
  26. 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
  27. 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
  28. van Buuren S. (2012): Flexible imputation of missing data. CRC press.10.1201/b11826
  29. Wright K. (2012): agridat: Agricultural datasets. R package version 1.4. http://CRAN.R-project.org/package=agridat>
  30. 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
  31. Yan W. (2013): Biplot analysis of incomplete two-way data. Crop Science 53(1): 48-57. 10.2135/cropsci2012.05.0301
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.