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Instrumental-Variable Calibration Estimation in Survey Sampling

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Authors
박승환
Advisor
전종우
Major
자연과학대학 통계학과
Issue Date
2013-08
Publisher
서울대학교 대학원
Keywords
Asymptotic design unbiasednessExponential tiltingRegression estimationWeighting
Description
학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2013. 8. 전종우.
Abstract
The prediction model, which makes effective use of auxiliary information available throughout the population, is often used to derive efficient estimation in survey sampling.
To protect from the failure of the assumed model, asymptotic design unbiasedness is often imposed in the prediction estimator.
An instrumental-variable calibration estimator can be considered to achieve the model optimality among the class of calibration estimators that is asymptotically design unbiased.

In this paper, we propose a new calibration estimator that is asymptotically equivalent to the optimal instrumental-variable calibration estimator.
The resulting weights are no smaller than one and can be constructed to achieve the range restrictions. The proposed method can be extended to calibration estimation under two-phase sampling. Some numerical results are presented using the real data example of the 1997 National Resource Inventory of the United States.
Language
English
URI
http://hdl.handle.net/10371/121143
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Ph.D. / Sc.D._통계학과)
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