NONPARAMETRIC ENDOGENOUS POST-STRATIFICATION ESTIMATION

被引:21
|
作者
Dahlke, Mark [1 ]
Breidt, F. Jay [1 ]
Opsomer, Jean D. [1 ]
Van Keilegom, Ingrid [2 ]
机构
[1] Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA
[2] Catholic Univ Louvain, Inst Stat, B-1348 Louvain La Neuve, Belgium
基金
美国国家科学基金会; 欧洲研究理事会;
关键词
Monotone regression; smoothing; survey estimation; INVENTORY DATA; FOREST;
D O I
10.5705/ss.2011.272
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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. Such "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, with particular emphasis on monotone regression models. Asymptotic properties of the nonparametric EPSE are investigated under a superpopulation model framework. Simulation experiments illustrate the practical effects of first fitting a nonparametric model to survey data before post-stratifying.
引用
收藏
页码:189 / 211
页数:23
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