|Title:||Shape constrained kernel density estimation|
|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.|
Nonparametric density estimation
|Appears in Collections:||Sonderforschungsbereich (SFB) 475|
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