Comparison of Two VA Laboratory Data Repositories Indicates That Missing Data Vary Despite Originating From the Same Source
نویسندگان
چکیده
منابع مشابه
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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OBJECTIVES Missing laboratory data is a common issue, but the optimal method of imputation of missing values has not been determined. The aims of our study were to compare the accuracy of four imputation methods for missing completely at random laboratory data and to compare the effect of the imputed values on the accuracy of two clinical predictive models. DESIGN Retrospective cohort analysi...
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چکیده ندارد.
15 صفحه اولDEA 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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The article is a letter to the editor, so it has no abstract.
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ژورنال
عنوان ژورنال: Medical Care
سال: 2009
ISSN: 0025-7079
DOI: 10.1097/mlr.0b013e31817d69c2