This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 551
Type: Invited
Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #306232
Title: Nonparametric Endogenous Poststratification Estimation
Author(s): Jay Breidt*+ and Mark Dahlke and Jean Opsomer and Ingrid Van Keilegom
Companies: Colorado State University and Colorado State University and Colorado State University and Université Catholique de Louvain
Address: 102 Statistics, Fort Collins, CO, 80523-1877,
Keywords: complex survey ; kernel regression ; penalized spline

Post-stratification is used to improve the precision of survey estimators when categorical auxiliary information is available from external sources. In natural resource surveys, such information may be obtained from remote sensing data classified into categories and displayed as maps. These maps may be based on classification models fitted to the sample data. Post-stratification of the sample based on categories derived from the sample data ("endogenous post-stratification") violates the standard assumptions that observations are classified without error into post-strata, and post-stratum population counts are known. Properties of the endogenous post-stratification estimator (EPSE) are derived for the case of sample-fitted nonparametric models. Asymptotic and finite-sample properties of the nonparametric EPSE are investigated under both design and model frameworks.

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