CAJ | 학술논문

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:(P)i＞0和∑i∈s(P)i=1.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(F)MKL(y)渐近等价于广义回归估计,且本身是一分函数布.
본문아문제출료사용조사수거중완전보조신식적모형교정K-L상대적최소화방법.재고계유한총체균치시아문적고계점근등개우MC고계(Wu and Sitter(2001)).아문방법일개유흡인력적우점시,도출적권구유특정:(P)i＞0화∑i∈s(P)i=1.저사득가파차방법응용우고계분포함수화분위수.도출적분포함수고계량(F)MKL(y)점근등개우엄의회귀고계,차본신시일분함수포.
We propose a model-calibrated K-L relative entropy minimization (MKLEM) approach to using complete auxiliary information from survey data.Our estimator is asymp totically equivalent to a model-calibration (MC) estimator in Wu and Sitter(2001)in the ease of estimating the finite population mean.One attractive advantage of our MKLEM approach is the intrinsic properties of the resulting weights (P)i＞0 and ∑i∈s(P)i=1,which make this approach generally applicable to the estimation of distribution functions and quantiles.The resulting estimator (F)MKL(y) is asymptotically equivalent to a generalized regression estimator and itself a distribution function.