Reducing variance in nonparametric surface estimation

Ming-Yen Cheng, Peter Hall

Research output: Contribution to journalArticlepeer-review

Abstract

We suggest a method for reducing variance in nonparametric surface estimation. The technique is applicable to a wide range of inferential problems, including both density estimation and regression, and to a wide variety of estimator types. It is based on estimating the contours of a surface by minimising deviations of elementary surface estimates along a quadratic curve. Once a contour estimate has been obtained, the final surface estimate is computed by averaging conventional surface estimates along a portion of the contour. Theoretical and numerical properties of the technique are discussed.
Original languageEnglish
Pages (from-to)375-397
Number of pages23
JournalJournal of Multivariate Analysis
Volume86
Issue number2
DOIs
Publication statusPublished - Aug 2003

User-Defined Keywords

  • Bandwidth
  • Boundary effect
  • Kernel method
  • Nonparametric density estimation
  • Nonparametric regression
  • Variance reduction

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