  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.
