The Use of Calibration Weighting for Variance Estimation Under Systematic Sampling: Applications to Forest Cover Assessment

The Use of Calibration Weighting for Variance Estimation Under Systematic Sampling: Applications... The purpose of this note is to propose a variance estimator under non-measurable designs that exploits the existence of an auxiliary variable well correlated with the survey variable of interest. Under non-measurable designs, the Sen–Yates–Grundy variance estimator generates a downward bias that can be reduced using a calibration weighting based on the auxiliary variable. Conditions of approximate unbiasedness for the resulting calibration estimator are given. The application to systematic sampling is considered. The proposal proves to be effective for estimating the variance of the forest cover estimator in remote sensing-based surveys, owing to the strong correlation between the reference data, available from a systematic sample, and the satellite map data, available for the whole population and hence exploited as an auxiliary variable. Supplementary materials accompanying this paper appear online. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Agricultural, Biological, and Environmental Statistics Springer Journals

The Use of Calibration Weighting for Variance Estimation Under Systematic Sampling: Applications to Forest Cover Assessment

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Publisher
Springer Journals
Copyright
Copyright © 2018 by International Biometric Society
Subject
Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Agriculture; Monitoring/Environmental Analysis; Biostatistics
ISSN
1085-7117
eISSN
1537-2693
D.O.I.
10.1007/s13253-018-0325-x
Publisher site
See Article on Publisher Site

Abstract

The purpose of this note is to propose a variance estimator under non-measurable designs that exploits the existence of an auxiliary variable well correlated with the survey variable of interest. Under non-measurable designs, the Sen–Yates–Grundy variance estimator generates a downward bias that can be reduced using a calibration weighting based on the auxiliary variable. Conditions of approximate unbiasedness for the resulting calibration estimator are given. The application to systematic sampling is considered. The proposal proves to be effective for estimating the variance of the forest cover estimator in remote sensing-based surveys, owing to the strong correlation between the reference data, available from a systematic sample, and the satellite map data, available for the whole population and hence exploited as an auxiliary variable. Supplementary materials accompanying this paper appear online.

Journal

Journal of Agricultural, Biological, and Environmental StatisticsSpringer Journals

Published: Jun 4, 2018

References

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