Multiple imputation and other resampling schemes for imputing missing observations

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Multiple imputation and other resampling schemes for imputing missing observations

The problem of imputing missing observations under the linear regression model is considered. It is assumed that observations are missing at random and all the observations on the auxiliary or independent variables are available. Estimates of the regression parameters based on singly and multiply imputed values are given. Jackknife as well as bootstrap estimates of the variance of the singly im...

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Multiple Imputation for Missing Data

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Correspondence *Corresponding author Email: [email protected] Presence of missing values in a dataset can adversely affect the performance of a classifier; it deteriorates rapidly as missingness increases. Single and Multiple Imputation (MI) are normally performed to fill in the missing values. In this paper, we present several variants of combining MI and bootstrapping to create ensembl...

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ژورنال

عنوان ژورنال: Journal of Multivariate Analysis

سال: 2009

ISSN: 0047-259X

DOI: 10.1016/j.jmva.2009.06.003