Almost Sure L(1)-Norm Convergence for Data-Based Histogram Density Estimates.

Abstract

The main result of this paper is summarized in Theorem 1, which states that when certain conditions of a general nature are satisfied, the data-based histogram density estimator is strongly consistent in the sense that the mean absolute deviation of the estimator and the density function converges to zero almost surely for any density function, as the sample size increases to infinity. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1986
Accession Number
ADA170059

Entities

People

  • L. C. Zhao
  • X. R. Chen

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Brownian Motion
  • Contracts
  • Convergence
  • Distribution Functions
  • Governments
  • Histograms
  • Intervals
  • Multivariate Analysis
  • National Governments
  • Probability
  • Random Variables
  • Scientific Research
  • Theorems
  • United States Government
  • Universities
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Statistical inference.