Wu, Changchun; Zhang, Runchu A model-calibration information-theoretic approach to using complete auxiliary information. (English) Zbl 1125.62003 J. Math. Res. Expo. 27, No. 1, 87-97 (2007). Summary: 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 of C. Wu and R. R. Sitter [J. Am. Stat. Assoc. 96, No. 453, 185–193 (2001; Zbl 1015.62005)] in the case of estimating the finite population mean. One attractive advantage of our MKLEM approach are 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. MSC: 62D05 Sampling theory, sample surveys 62G05 Nonparametric estimation Keywords:K-L relative entropy; generalized regression estimator; empirical likelihood Citations:Zbl 1015.62005 PDFBibTeX XMLCite \textit{C. Wu} and \textit{R. Zhang}, J. Math. Res. Expo. 27, No. 1, 87--97 (2007; Zbl 1125.62003)