Nearest neighbor imputation algorithms: a critical evaluation
نویسندگان
چکیده
منابع مشابه
Nearest neighbor imputation algorithms: a critical evaluation
BACKGROUND Nearest neighbor (NN) imputation algorithms are efficient methods to fill in missing data where each missing value on some records is replaced by a value obtained from related cases in the whole set of records. Besides the capability to substitute the missing data with plausible values that are as close as possible to the true value, imputation algorithms should preserve the original...
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Sample surveys typically gather information on a sample of units from a finite population and assign survey weights to the sampled units. Survey frequently have missing values for some variables for some units. Fractional regression imputation creates multiple values for each missing value by adding randomly selected empirical residuals to predicted values. Fractional imputation methods assign ...
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The (k-)nearest neighbor searching has very high computational costs. The algorithms presented for nearest neighbor search in high dimensional spaces have have suffered from curse of dimensionality, which affects either runtime or storage requirements of the algorithms terribly. Parallelization of nearest neighbor search is a suitable solution for decreasing the workload caused by nearest neigh...
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Nearest neighbor imputation (NNI) is a popular imputation method used to compensate for item nonresponse in sample surveys. Although previous results showed that the NNI sample mean and quantiles are consistent estimators of the population mean and quantiles, large sample inference procedures, such as asymptotic confidence intervals for the population mean and quantiles, are not available. For ...
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ژورنال
عنوان ژورنال: BMC Medical Informatics and Decision Making
سال: 2016
ISSN: 1472-6947
DOI: 10.1186/s12911-016-0318-z