Abstract
A kernel density estimator, constructed from a combination of disaggregate data subject to sampling bias and aggregate data, is described. The asymptotic performance of the estimator is explored, and details of an algorithm for its implementation are given. The issue of bandwidth selection is addressed. Use of the estimator is illustrated through two examples. The first involves simulated data while the second example concerns traffic speed data collected by automatic vehicle detectors on Interstate 5 near Seattle.
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