期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 134, 期 -, 页码 23-33出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2014.02.007
关键词
Missing data; Single imputation; Expectation-maximization; Multiple imputation; Air quality
类别
资金
- European Union (FEDER) [UNLC00-23-003]
- Spanish Ministry of Science and Innovation (Research Grant) [CGL2010-18145]
- Galician Government [10MSD164019PR, 2010/52]
- Xunta of Galicia by an I2C (Type B) postdoctoral Grant
- EU-FEDER program
Datasets with missing data ratios ranging from 24% to 4%, corresponding to three air quality monitoring studies, were used to ascertain whether major differences occur when five currently used imputation methods are applied (four single imputation methods and a multiple imputation one). Unrotated and Varimax-rotated factor analyses performed on the imputed datasets were compared. All methods performed similarly, although multiple imputation yielded more disperse imputed values. Main differences occurred when a variable with missing values correlated poorly to the other features and when a variable had relevant loadings in several unrotated factors, which sometimes changed the order of the rotated factors. (C) 2014 Elsevier B.V. All rights reserved.
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