Density estimation with heteroscedastic error

dc.creatorDelaigle, Aurore
dc.creatorMeister, Alexander
dc.date2008-05-15
dc.date.accessioned2026-07-07T12:18:53Z
dc.date.available2026-07-07T12:18:53Z
dc.descriptionIt is common, in deconvolution problems, to assume that the measurement errors are identically distributed. In many real-life applications, however, this condition is not satisfied and the deconvolution estimators developed for homoscedastic errors become inconsistent. In this paper, we introduce a kernel estimator of a density in the case of heteroscedastic contamination. We establish consistency of the estimator and show that it achieves optimal rates of convergence under quite general conditions. We study the limits of application of the procedure in some extreme situations, where we show that, in some cases, our estimator is consistent, even when the scaling parameter of the error is unbounded. We suggest a modified estimator for the problem where the distribution of the errors is unknown, but replicated observations are available. Finally, an adaptive procedure for selecting the smoothing parameter is proposed and its finite-sample properties are investigated on simulated examples.
dc.descriptionPublished in at http://dx.doi.org/10.3150/08-BEJ121 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
dc.identifierhttps://arxiv.org/abs/0805.2216
dc.identifierhttp://arxiv.org/abs/0805.2216
dc.identifierBernoulli 2008, Vol. 14, No. 2, 562-579
dc.identifierdoi:10.3150/08-BEJ121
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212566
dc.subjectStatistics Theory
dc.titleDensity estimation with heteroscedastic error
dc.typetext

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