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• W789966232 abstract "Various methods of estimating missing values in highly correlated climatological data are examined. Any generalised method is likely to be based on a correlation matrix, but the incompleteness of the data introduces problems with this approach. These are illustrated by program BMDPAM of the BMDP suite, which produces estimates worse than those using traditional methods based on single station comparisons. Principal component analysis is considered likely to be the best statistical tool for estimating missing values among highly correlated data. The high quality correlation matrix recuired as input can be obtained by using a simple estimating procedure to produce a preliminary set of complete data. A simple technique was devised for climatological data in the UK, and was found to give estimates similar to those obtained from an eigenvector scheme used for quality control purposes." @default.
• W789966232 created "2016-06-24" @default.
• W789966232 creator A5078531099 @default.
• W789966232 date "1982-01-01" @default.
• W789966232 modified "2023-09-24" @default.
• W789966232 title "The Estimation of Missing Values in Highly Correlated Data" @default.
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• W789966232 doi "https://doi.org/10.1007/978-3-642-51461-6_65" @default.
• W789966232 hasPublicationYear "1982" @default.
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