A Law of the Iterated Logarithm for Non-Parametric Regression Function Estimators.

Abstract

We prove a law of the iterated logarithm for nonparametric regression function estimators using strong approximations to the two dimensional empirical process. We consider the case of Nadaraya-Watson kernel estimators and of estimators based on orthogonal polynomials when the marginal density of the design variable X is unknown or known. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1983
Accession Number
ADA133260

Entities

People

  • Wolfgang Hardle

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Classification
  • Consistency
  • Contracts
  • Data Science
  • Delta Functions
  • Information Science
  • Normal Distribution
  • North Carolina
  • Polynomials
  • Probability
  • Random Variables
  • Security
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Statistical inference.