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Activity Number: 423 - Contributed Poster Presentations: Survey Research Methods Section
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #305196
Title: Functional Covariate Adjustment in Survey Sampling
Author(s): Hengfang Wang* and Zhengyuan Zhu and Jae-kwang Kim
Companies: Iowa State University of Science and Technology and Iowa State University and Iowa State University
Keywords: Kernel method; Eigenvalue decomposition; Reproducing kernel Hilbert Space; Sobolev space; Empirical process

Calibration estimation is widely used when researchers want to estimate the population mean with the adjustment of the survey weights when auxiliary variables are available. However, before such calibration estimation, several functions for calibration equations have to be determined. In this paper, we utilize a reproducing kernel Hilbert space to do the approximation between sample space and population space of covariate to get such adjustment of survey weights without predetermining functions. In addition, such infinite-dimensional approximation problem has a finite-dimensional representation when we do the optimizations. The convergence rate of our proposed estimators have been studied. Numerical studies have been done to illustrate the performance of our proposed estimator.

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

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