Details

Autor(en) / Beteiligte

• Arciniegas-Alarcón, Sergio
• García-Peña, Marisol
• Krzanowski, Wojtek J.
• Rengifo, Camilo

Titel

Missing value imputation in a data matrix using the regularised singular value decomposition

Ist Teil von

• MethodsX, 2023-12, Vol.11, p.102289-102289, Article 102289

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Some statistical analysis techniques may require complete data matrices, but a frequent problem in the construction of databases is the incomplete collection of information for different reasons. One option to tackle the problem is to estimate and impute the missing data. This paper describes a form of imputation that mixes regression with lower rank approximations. To improve the quality of the imputations, a generalisation is proposed that replaces the singular value decomposition (SVD) of the matrix with a regularised SVD in which the regularisation parameter is estimated by cross-validation. To evaluate the performance of the proposal, ten sets of real data from multienvironment trials were used. Missing values were created in each set at four percentages of missing not at random, and three criteria were then considered to investigate the effectiveness of the proposal. The results show that the regularised method proves very competitive when compared to the original method, beating it in several of the considered scenarios. As it is a very general system, its application can be extended to all multivariate data matrices. [Display omitted]