JOS

Abstract
Journal of Official Statistics, Vol.23, No.3, 2007. pp. 371386

Contents
Current Issue
Personal Reference Library (PRL)
Personal Page
Archive
Search
Home


Resampling Variance Estimation in Surveys with Missing Data

Abstract:
We discuss variance estimation by resampling in surveys in which data are missing. We derive a formula for linearization in the case of calibrated estimation with deterministic regression imputation, and compare the resulting variance estimates with balanced repeated replication with and without grouping, the bootstrap, the block jackknife, and multiple imputation, for simulated data based on the Swiss Household Budget Survey. We also investigate the number of replications needed for reliable variance estimation under resampling in this context. Linearization, the bootstrap, and multiple imputation perform best in terms of relative bias and mean squared error.

Keywords:
Balanced repeated replication, bootstrap, calibration, influence function, jackknife, linearization, missing data, multiple imputation

Copyright Statistics Sweden 1996-2018.  Open Access
ISSN 0282-423X
Created and Maintained by OKS Group