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Activity Number:
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197
- SPAAC Poster Competition
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #322611
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Title:
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Penalized Weight Calibration: Application to Neural Network via Ridge Approximation
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Author(s):
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Yonghyun Kwon* and Jae-kwang Kim
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Companies:
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Iowa State University and Iowa State University
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Keywords:
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Calibration;
Nonparametric regression;
Neural network;
SCAD;
Ridge regression;
Design-based estimation
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Abstract:
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In survey sampling, auxiliary information is often used to improve the precision of the design-based estimates. One way to include auxiliary information is calibration but calibrating on many variables may sacrifice the accuracy of the resulting estimator. In this paper, we propose a calibration weighting method based on penalization. This penalized calibration estimator can automatically and simultaneously adjust the importance of the variable according to its power of explanation. Also, we show that ridge approximation of SCAD estimator is strongly related to soft calibration for estimating population total. The proposed idea can be extended to a non-linear class of basis functions such as neural network.
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Authors who are presenting talks have a * after their name.
