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- Title
Missing‐value imputation using the robust singular‐value decomposition: Proposals and numerical evaluation.
- Authors
García‐Peña, Marisol; Arciniegas‐Alarcón, Sergio; Krzanowski, Wojtek J.; Duarte, Diego
- Abstract
A common problem in the analysis of data from multi‐environment trials is imbalance caused by missing observations. To get around this problem, Yan proposed a method for imputing the missing values based on the singular‐value decomposition (SVD) of a matrix. However, this SVD can be affected by outliers and produce low quality imputations. In this article, we propose four extensions of the Yan method that are resistant to outliers, replacing the standard SVD method with four robust SVD extensions. We evaluate these methods, using exclusively numerical criteria in a simulation study and in a cross‐validation study based on real data. We conclude that in the presence of outliers, the standard SVD method should not be used; instead, the best alternatives are the robust SVD methods based on sub‐sampling when the percentage of contamination is less than 2% following a completely random missing data mechanism. In any other case, methods that either minimize the L2 norm or that involve L1 regressions are preferable. Core Ideas: Presents new robust imputation methods to solve the unbalance problem in two‐way data matricesThe proposed methodologies use robust singular‐value decomposition to produce the imputationsProposed methods do not have structural assumptions and can be applied in multi‐environment trialsThere has not yet been a study that proposes a robust version of the Yan imputation system
- Subjects
PROBLEM solving; MISSING data (Statistics); MULTIPLE imputation (Statistics); DATA analysis; COMPUTER simulation; DATABASES
- Publication
Crop Science, 2021, Vol 61, Issue 5, p3288
- ISSN
0011-183X
- Publication type
Article
- DOI
10.1002/csc2.20508