Nonlinear Parameterization in Bi-Criteria Sample Balancing
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May 1, 2010
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Stan Lipovetsky
GfK Custom Research North America, Minneapolis
Abstract
Sample balancing is widely used in applied research to adjust a sample data to achieve better correspondence to Census statistics. The classic Deming-Stephan iterative proportional approach finds the weights of observations by fitting the cross-tables of sample counts to known margins. This work considers a bi-criteria objective for finding weights with maximum possible effective base size. This approach is presented as a ridge regression with the exponential nonlinear parameterization that produces nonnegative weights for sample balancing.
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