Deconvolution boundary kernel method in nonparametric density estimation

Shunpu Zhang, Rohana J. Karunamuni

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

This paper considers the nonparametric deconvolution problem when the true density function is left (or right) truncated. We propose to remove the boundary effect of the conventional deconvolution density estimator by using a special class of kernels: the deconvolution boundary kernels. Methods for constructing such kernels are provided. The mean squared error properties, including the rates of convergence, are investigated for supersmooth and ordinary smooth errors. Numerical simulations show that the deconvolution boundary kernel estimator successfully removes the boundary effects of the conventional deconvolution density estimator.

Original languageEnglish (US)
Pages (from-to)2269-2283
Number of pages15
JournalJournal of Statistical Planning and Inference
Volume139
Issue number7
DOIs
StatePublished - Jul 1 2009

Keywords

  • Boundary kernel function
  • Deconvolution
  • Fourier transformation
  • Global optimal bandwidth
  • Nonparametric density estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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