Arbeitspapier

A note on uniform consistency of monotone function estimators

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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.

Sprache
Englisch

Erschienen in
Series: Technical Report ; No. 2005,35

Thema
function estimator
kernel method
monotonicity
uniform convergence
Regression
Nichtparametrisches Verfahren
Schätztheorie
Theorie

Ereignis
Geistige Schöpfung
(wer)
Neumeyer, Natalie
Ereignis
Veröffentlichung
(wer)
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
(wo)
Dortmund
(wann)
2005

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Neumeyer, Natalie
  • Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen

Entstanden

  • 2005

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