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Activity Number: 508 - Leading the Estimates Towards Known Benchmarks
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #329395 Presentation
Title: A Global Convergent Algorithm for Integer Calibration Weighting
Author(s): Kelly Toppin* and Luca Sartore and Clifford Spiegelman
Companies: National Agricultural Statistics Service and National Institute of Statistical Sciences and Texas A&M University
Keywords: DSE; ; Weights; ; Global integer optimization; Census of Agriculture; session ID # 215233
Abstract:

The USDA's National Agricultural Statistics Service (NASS) conducts the U.S. Census of Agriculture in years ending in 2 and 7. The census describes the characteristics of U.S. farms and the people who operate them. To adjust for under-coverage, nonresponse and misclassification, NASS produces the weights on the responding records using a capture-recapture methodology. However, the weights need to be further refined through a calibration process so that the census estimates agree with known population values. The current algorithm (called INCA) was developed to provide integer calibrated weights per NASS requirements. In INCA, weights adjusted for undercoverage, nonresponse, and misclassification are first rounded using an optimal rounding procedure, and then integer programming using coordinate descent is performed on the integer weights. However, the existence of multiple local solutions makes the search of a global solution exponentially complex. This article describes a global optimization algorithm for integer calibration based on an L1-norm relative error. The results of a simulation study designed to investigate the properties of the estimator is presented. SID # 215233


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