Confidence bands in density estimation
The Annals of Statistics · 2010 · Volume 38 · Issue 2
Abstract
Given a sample from some unknown continuous density f : ℝ→ℝ, we construct adaptive confidence bands that are honest for all densities in a “generic” subset of the union of t-Hölder balls, 0<t≤r, where r is a fixed but arbitrary integer. The exceptional (“nongeneric”) set of densities for which our results do not hold is shown to be nowhere dense in the relevant Hölder-norm topologies. In the course of the proofs we also obtain limit theorems for maxima of linear wavelet and kernel density estimators, which are of independent interest.
Keywords
60F05, 62G07, adaptive estimation, Density estimation, Extremes, Gaussian processes, kernel estimators, limit theorem, wavelet estimators
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