JSM 2005 - Toronto

Abstract #303486

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 445
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #303486
Title: Calibration Adjustments when Not All Targets Can Be Met
Author(s): Matthew J. Fetter and James Gentle*+ and Charles R. Perry
Companies: National Agricultural Statistics Service and George Mason University and National Agricultural Statistics Service
Address: School of Computational Sciences, Fairfax, VA, 22030,
Keywords: calibration ; weighting ; optimization ; undercoverage
Abstract:

Calibration can be used to improve survey estimates by adjustment of the initial weights so as to satisfy calibration equations. The standard methods involve a minimization problem with an objective function that measures the overall change in the initial weights. The minimization problem usually is constrained to require the calibrated weights be within some prescribed range. In addition, these weights are sometimes required to be integers. If there are several calibration variables, the problem may be infeasible. In that case, either constraints must be relaxed or else not all of the calibration targets can be met. A simple approach is to replace calibration target values with target ranges. The first problem with this approach is how to choose the ranges, but then even with ranges instead of fixed points, the constrained minimization problem may remain infeasible. Another approach is to incorporate into the objective function a component that measures the overall amount by which the calibration estimates miss the calibration targets. We formulate a general optimization problem for this approach.

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