Shape constrained kernel density estimation
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Date
2008-11-26T14:38:01Z
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Abstract
In this paper, a method for estimating monotone, convex and log-concave densities is
proposed. The estimation procedure consists of an unconstrained kernel estimator which is
modified in a second step with respect to the desired shape constraint by using monotone rearrangements. It is shown that the resulting estimate is a density itself and shares the asymptotic properties of the unconstrained estimate. A short simulation study shows the finite sample behavior.
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Keywords
Convexity, Log-concavity, Monotone rearrangement, Monotonicity, Nonparametric density estimation