Abstract
Richard Valliant (1993) "Post
Stratification and Conditional Variance Estimation," Journal
of the American Statistical Association, 88, accepted for
publication.
Post stratification is a commonly used estimation
technique in sample surveys. After selection of a sample,
units are cross-classified into post strata for which known
census totals of units are available, and estimates of
population totals are obtained in each cell. The technique is
used as a means of reducing bias due to poor frame coverage
and of reducing variance through stratification.
Conditionality arguments imply that inferences using a post
averaging over all distributions that might have occurred in
a random sampling plan. This paper examines the
linearization, balanced repeated replication, and other
variance estimators in stratified two-stage sampling to
determine whether they estimate conditional or unconditional
variances. Theoretical work is supported by a simulation
study using data from the U.S. Current Population Survey.
Last Modified Date: July 19, 2008
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