利用完全辅助信息的模型校正信息论方法

A Model-Calibration Information-Theoretic Approach to Using Complete Auxiliary Information

本文我们提出了使用调查数据中完全辅助信息的模型校正K-L相对熵最小化方法．在估计有限总体均值时我们的估计渐近等价于MC估计(Wu and Sitter(2001))．我们方法一个有吸引力的优点是,导出的权具有特征:pi>0和■pi=0 ．这使得可把此方法应用于估计分布函数和分位数．导出的分布函数估计量FMKL(y)渐近等价于广义回归估计,且本身是一分函数布．

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： ^pi〉0和∑i∈s^pi=1, which make this approach generally applicable to the estimation of distribution functions and quantiles. The resulting estimator ^FMKL（y） is asymptotically equivalent to a generalized regression estimator and itself a distribution function.