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Activity Number: 210 - Contributed Poster Presentations: Survey Research Methods Section
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Survey Research Methods Section
Abstract #312267
Title: Nonparametric Functional Calibration Estimation in Survey Sampling
Author(s): Hengfang Wang* and Jae-kwang Kim and Zhengyuan Zhu
Companies: Iowa State University of Science and Technology and Iowa State University and Iowa State University
Keywords: kernel method; reproducing kernel Hilbert space; eigendecomposition; doubly-robust; probability sample
Abstract:

Calibration estimation, a technique of adjusting the sampling weights to match the unknown population totals of auxiliary variables, is a popular method of estimation in survey sampling. When the auxiliary vairbales are observed for all units in the finite population, one can apply the model calibration of Wu and Sitter (2001) using the working outcome model. In this paper, we develope a kernel-based nonparametric calibration method that does not require an explicit outcome model. The proposed method is a function calibration employing infinite-dimensional reproducing kernel Hilbert space (RKHS). Numerical algorithms are developed and implemented to solve the optimization problem in the function calibration, and some asymptotic results are presented as well. Furthermore, under the nonparametric working model, the proposed calibration estimator attains the Godambe-Joshi lower bound asymptotically. Simulation results are presented to compare the proposed method with other calibration methods.


Authors who are presenting talks have a * after their name.

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