Fitting Distributions to Data, A Comparison of Two Methods.

Abstract

Monte Carlo methods are used to compare the methods of maximum likelihood and least squares to estimate a cumulative distribution function. When the probabilistic model used is correct or nearly correct, the two methods produce similar results with the MLE usually slightly superior. When an incorrect model is used, or when the data is contaminated, the least squares technique often gives substantially superior results. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 30, 1981
Accession Number
ADA102683

Entities

People

  • Paul N. Somerville
  • Steven J. Bean

Organizations

  • University of Central Florida

Tags

DTIC Thesaurus Topics

  • Air Force
  • Distribution Functions
  • Mathematics
  • Models
  • Normal Distribution
  • Numbers
  • Probabilistic Models
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Real Numbers
  • Security
  • Statistical Analysis
  • Statistical Samples
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.