Missing data

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Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

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dea with missing data: an interval data assignment approach

in the classical data envelopment analysis (dea) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. in recent years, there are few researches on handling missing data. this paper suggests a new interval based approach to apply missing data, which is the modified version of kousmanen (2009) approach. first, the prop...

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Missing data.

424 BMJ | 24 FEBRUARY 2007 | VOLUME 334 Almost all studies have some missing observations. Yet textbooks and software commonly assume that data are complete, and the topic of how to handle missing data is not often discussed outside statistics journals. There are many types of missing data and different reasons for data being missing. Both issues affect the analysis. Some examples are: (1) In a...

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ژورنال

عنوان ژورنال: Phlebology: The Journal of Venous Disease

سال: 2011

ISSN: 0268-3555,1758-1125

DOI: 10.1258/phleb.2011.011j03