Quantization for nonparametric regression

László Györfi, Marten Wegkamp

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

The authors discuss quantization or clustering of nonparametric regression estimates. The main tools developed are oracle inequalities for the rate of convergence of constrained least squares estimates. These inequalities yield fast rates for both nonparametric (unconstrained) least squares regression and clustering of partition regression estimates and plug-in empirical quantizers. The bounds on the rate of convergence generalize known results for bounded errors to subGaussian, too.

Original languageEnglish
Pages (from-to)867-874
Number of pages8
JournalIEEE Transactions on Information Theory
Volume54
Issue number2
DOIs
Publication statusPublished - Feb 1 2008

Keywords

  • Finite-sample bounds
  • Least squares estimates
  • Regression estimation with restriction
  • Vector quantization

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

Fingerprint Dive into the research topics of 'Quantization for nonparametric regression'. Together they form a unique fingerprint.

  • Cite this