Data-driven boundary estimation in deconvolution problems

A Delaigle, I Gijbels

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)


Estimation of the support of a density function is considered, when only a contaminated sample from the density is available. A kernel-based method has been proposed in the literature, where the authors study theoretical bias and variance of the estimator. Practical implementation issues of this method are considered here, which are a necessary supplement to the theoretical results to get to a data-driven method that is widely applicable. Two such practical data-driven procedures are proposed. Simulation results show that they perform well for a wide variety of densities (including quite difficult cases). The methods can also be applied for error-free data and as such also present data-driven procedures for estimation of boundaries in the case of non-contaminated data. Moreover they can be applied for estimating discontinuities of a density, as is shown. The proposed data-driven boundary estimation procedures are illustrated in frontier estimation. (c) 2005 Elsevier B.V. All rights reserved.
Translated title of the contributionData-driven boundary estimation in deconvolution problems
Original languageEnglish
Pages (from-to)1965 - 1994
JournalComputational Statistics and Data Analysis
Volume50 (8)
Publication statusPublished - 10 Apr 2006

Bibliographical note

Publisher: Elsevier Science BV
Other identifier: IDS number 021YL


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