Abstract
Robert J. Casady and Richard Valliant
(1993) "Conditional Properties of Post-Stratified Estimators
Under Normal Theory", Survey Methodology, Vol.IXX.2.
Post-stratification is a common technique for improving
precision of estimators by using data items not available at
the design stage of a survey. In large, complex samples, the
vector of Horvitz-Thompson estimators of survey target
variables and of post-stratum population sizes will, under
appropriate conditions, be approximately multivariate normal.
This large sample normality leads to a new post-stratified
regression estimator, which is analogous to the linear
regression estimator in simple random sampling. We derive the
large sample design bias and mean squared errors of this new
estimator, the standard post-stratified estimator, the
Horvitz-Thompson estimator, and a ratio estimator. We use
both real and artificial populations to study empirically the
conditional and unconditional properties of the estimators in
multistage sampling.
Last Modified Date: July 19, 2008
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