标题： 非参数加性模型辅助的调查数据分析方法 显示英文标题

标题： Nonparametric Additive Model-assisted Estimation for Survey Data

摘要： 研究了一种加性模型辅助的非参数方法，借助辅助信息来估计大规模调查数据的有限总体总值。 提出了一类估计量，通过结合样条和局部多项式平滑方法来提高著名的霍维茨-汤普森估计量的精度。 这些估计量在一致校准、渐近设计无偏、相合、正态分布且稳健方面具有优势，即在渐近意义上达到了戈达姆-乔希方差预测下界。 进一步发展了一种一致的模型选择程序以选取显著的辅助变量。 所提出的这种方法足够快速，在几秒钟内即可分析高维大型调查数据。 通过模拟研究实证评估了所提出方法的表现。 显示英文摘要

摘要： An additive model-assisted nonparametric method is investigated to estimate the finite population totals of massive survey data with the aid of auxiliary information. A class of estimators is proposed to improve the precision of the well known Horvitz-Thompson estimators by combining the spline and local polynomial smoothing methods. These estimators are calibrated, asymptotically design-unbiased, consistent, normal and robust in the sense of asymptotically attaining the Godambe-Joshi lower bound to the anticipated variance. A consistent model selection procedure is further developed to select the significant auxiliary variables. The proposed method is sufficiently fast to analyze large survey data of high dimension within seconds. The performance of the proposed method is assessed empirically via simulation studies.