A Model-Calibration Information-Theoretic Approach to Using Complete Auxiliary Information |
Received:September 21, 2004 Revised:July 02, 2006 |
Key Words:
model-calibration complete auxiliary information K-L relative entropy generalized regression estimator empirical likelihood.
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Fund Project:the National Natural Science Foundation of China (10571093); the Scientific Research Fund of Zhejiang Provincial Education Department (20061599). |
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Abstract: |
We propose a model-calibrated K-L relative entropy minimization (MKLEM) approach to using complete auxiliary information from survey data. Our estimator is asymptotically equivalent to a model-calibration (MC) estimator in Wu and Sitter (2001) in the case of estimating the finite population mean. One attractive advantage of our MKLEM approach is the intrinsic properties of the resulting weights: $\hat{p}_{i}>0$ and $\sum_{i\in s}\hat{p}_{i}=1$, which make this approach generally applicable to the estimation of distribution functions and quantiles. The resulting estimator $\hat{F}_{MKL}(y)$ is asymptotically equivalent to a generalized regression estimator and itself a distribution function. |
Citation: |
DOI:10.3770/j.issn:1000-341X.2007.01.013 |
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