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

Lorenzo Fattorini (), Timothy G. Gregoire and Sara Trentini
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Lorenzo Fattorini: University of Siena
Timothy G. Gregoire: Yale University
Sara Trentini: University of Siena

Journal of Agricultural, Biological and Environmental Statistics, 2018, vol. 23, issue 3, No 3, 358-373

Abstract: 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.

Keywords: Calibration estimation; Forest cover estimation; Non-measurable designs; Pseudo-designs (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13253-018-0325-x

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