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Alleviating Linear Ecological Bias and Optimal Design with Subsample Data

Adam N. Glynn, Jon Wakefield, Mark S. Handcock and Thomas S. Richardson
Journal of the Royal Statistical Society. Series A (Statistics in Society)
Vol. 171, No. 1 (2008), pp. 179-202
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/30130736
Page Count: 24
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Alleviating Linear Ecological Bias and Optimal Design with Subsample Data
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Abstract

We illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides two main benefits. First, by including the individual level subsample data, the biases that are associated with linear ecological inference can be eliminated. Second, available ecological data can be used to design optimal subsampling schemes that maximize information about parameters. We present an application of this methodology to the classic problem of estimating the effect of a college degree on wages, showing that small, optimally chosen subsamples can be combined with ecological data to generate precise estimates relative to a simple random subsample.

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