133,760 research outputs found
Exact balanced random imputation for sample survey data
Surveys usually suffer from non-response, which decreases the effective
sample size. Item non-response is typically handled by means of some form of
random imputation if we wish to preserve the distribution of the imputed
variable. This leads to an increased variability due to the imputation
variance, and several approaches have been proposed for reducing this
variability. Balanced imputation consists in selecting residuals at random at
the imputation stage, in such a way that the imputation variance of the
estimated total is eliminated or at least significantly reduced. In this work,
we propose an implementation of balanced random imputation which enables to
fully eliminate the imputation variance. Following the approach in Cardot et
al. (2013), we consider a regularized imputed estimator of a total and of a
distribution function, and we prove that they are consistent under the proposed
imputation method. Some simulation results support our findings
On the regression method of estimation of population mean from incomplete survey data through imputation
When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation. The performance properties of the estimators based on the methods of mean imputation, regression imputation and no imputation are analyzed and the superiority of one method over the other is examined
- …