Empirical Sampling Study of a Goodness of Fit Statistic for Density Function Estimation

Abstract

The distribution of a measure of the distance between a probability density function and its estimate is examined through empirical sampling methods. The estimate of the density function is that proposed by Rosenblatt using sums of weight functions centered at the observed values of the random variables. The weight function in all cases was triangular, but both uniform and Cauchy densities were tried for different sample sizes and bandwidths. The simulated distributions look as if they could be approximated by Gamma distributions, in many cases. Some assessment can also be made of the rate of convergence of the moments and the distribution of the measure to the limiting moments and distribution, respectively.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1975
Accession Number
ADA012465

Entities

People

  • D. W. Robinson
  • L. H. Liu
  • M. Rosenblatt
  • P. W. Lewis

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Bandwidth
  • California
  • Convergence
  • Data Science
  • Grids
  • Histograms
  • Information Science
  • Normality
  • Operations Research
  • Probability
  • Probability Density Functions
  • Random Variables
  • Sampling
  • Security
  • Simulations
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.