Nonparametric Estimation with Local Rules.

Abstract

An attempt was made to identify the questions which are of genuine importance to a statistician who is interested in evaluating nonparametric estimation rules. The asymptotic performance of various nearest neighbor rules and loss functions was analyzed, and the results concerning the convergence of the conditional risk are quite strong considering the simple nature of the rules and the minimal assumptions made concerning the problem structure. In addition, distribution-free bounds on the error of two different estimates of finite sample performance are derived for a class of estimation rules which include k-nearest neighbor rules. For comparison purposes, a simulation study was carried out so that the bounds for a few specific distributions could be compared with the theoretical bounds.

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Document Details

Document Type
Technical Report
Publication Date
Oct 11, 1976
Accession Number
ADA035145

Entities

People

  • C. S. Penrod
  • T. J. Wagner

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Convergence

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms