Title: Data-driven boundary estimation in deconvolution problems
Authors: Delaigle, A
Gijbels, Irène # ×
Issue Date: 2006
Publisher: Elsevier science bv
Series Title: Computational statistics & data analysis vol:50 issue:8 pages:1965-1994
Abstract: 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.
ISSN: 0167-9473
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Statistics Section
× corresponding author
# (joint) last author

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