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Distribution-free multiple imputation in an interaction matrix through singular value decomposition Scientia Agricola
Bergamo,Genevile Carife; Dias,Carlos Tadeu dos Santos; Krzanowski,Wojtek Janusz.
Some techniques of multivariate statistical analysis can only be conducted on a complete data matrix, but the process of data collection often misses some elements. Imputation is a technique by which the missing elements are replaced by plausible values, so that a valid analysis can be performed on the completed data set. A multiple imputation method is proposed based on a modification to the singular value decomposition (SVD) method for single imputation, developed by Krzanowski. The method was evaluated on a genotype × environment (G × E) interaction matrix obtained from a randomized blocks experiment on Eucalyptus grandis grown in multienvironments. Values of E. grandis heights in the G × E complete interaction matrix were deleted randomly at three...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Missing data; Nonparametric; Eigenvalue; Eigenvector; Genotype-environment.
Ano: 2008 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000400015
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