Missing Data and Imputation Methods
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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Bayesian multiple imputation and maximum likelihood provide useful strategy for dealing with dataset including missing values. Imputation methods affect the significance of test results and the quality of estimates. In this paper, the general procedures of multiple imputation and maximum likelihood described which include the normal-based analysis of a multiple imputed dataset. A Monte Carlo si...
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Most ecological data sets contain missing values, a fact which can cause problems in the analysis and limit the utility of resulting inference. However, ecological data also tend to be spatially correlated, which can aid in estimating and imputing missing values. We compared four existing methods of estimating missing values: regression, kernel smoothing, universal kriging, and multiple imputat...
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BACKGROUND AND OBJECTIVES Missing information is inevitable in longitudinal studies, and can result in biased estimates and a loss of power. One approach to this problem is to impute the missing data to yield a more complete data set. Our goal was to compare the performance of 14 methods of imputing missing data on depression, weight, cognitive functioning, and self-rated health in a longitudin...
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ژورنال
عنوان ژورنال: Anesthesia & Analgesia
سال: 2020
ISSN: 0003-2999
DOI: 10.1213/ane.0000000000005068