Authors: Neumeyer, Natalie
Title: A note on uniform consistency of monotone function estimators
Language (ISO): en
Abstract: Recently, Dette, Neumeyer and Pilz (2005a) proposed a new monotone estimator for strictly increasing nonparametric regression functions and proved asymptotic normality. We explain two modifications of their method that can be used to obtain monotone versions of any nonparametric function estimators, for instance estimators of densities, variance functions or hazard rates. The method is appealing to practitioners because they can use their favorite method of function estimation (kernel smoothing, wavelets, orthogonal series,...) and obtain a monotone estimator that inherits desirable properties of the original estimator. In particular, we show that both monotone estimators share the same rates of uniform convergence (almost sure or in probability) as the original estimator.
Subject Headings: function estimator
kernel method
monotonicity
uniform convergence
URI: http://hdl.handle.net/2003/21639
http://dx.doi.org/10.17877/DE290R-14495
Issue Date: 2005-10-11T14:37:57Z
Appears in Collections:Sonderforschungsbereich (SFB) 475

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