JOS

Abstract
Journal of Official Statistics, Vol.12, No.3, 1996. pp. 225240

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


Limitations of Balanced Half-Sampling

Abstract:
Balanced half-sample (BHS) variance estimation is a popular technique among survey statisticians, but it has limitations. These limits are studied theoretically through a model-based approach and illustrated with simulations using artificial and real populations. In the fully balanced case, under a model often used for stratified, clustered populations, BHS produces a model-unbiased variance estimator for only one member of a broad class of estimators of totals. Another implementation of BHS variance estimation in large, complex surveys is to use partial balancing or grouping of strata to reduce the number of resample estimates that must be calculated. Instead of selecting a fully balanced, orthogonal set of half-samples, strata are combined into groups and a set of half-samples only large enough to be balanced on the groups is selected. For two-stage cluster samples either with or without poststratification this leads to an inconsistent variance estimator.

Keywords:
Balanced repeated replication; inconsistent variance estimator; model-based sampling; partial balancing; poststratification; two-stage cluster sampling.

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