A Bivariate Test of Goodness of Fit Based on a Gradually Increasing Number of Order Statistics

Abstract

An important area of Statistical Inference relates to the problem of assessing the conformity or goodness of fit of some observations to a null hypothesis. This hypothesis may be that the observations came from a distribution which belongs to a given family or may be a completely specified distribution. A statistical procedure which solves such a problem is called a test of goodness of fit. This thesis is concerned with the problem of testing the goodness of fit of a sample drawn from a continuous bivariate distribution, where the null hypothesis is that the true distribution is a completely specified one. A test is developed, where the test criterion is based in some functions of a subset of order statistics, functions which depend on the distribution under the null hypothesis.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1975
Accession Number
ADA008205

Entities

People

  • Jose Kreimerman

Organizations

  • Cornell University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Chi Square Test
  • Data Science
  • Distribution Functions
  • Distribution Theory
  • Information Science
  • Insensitive Explosives
  • Military Research
  • Normality
  • Order Statistics
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Real Numbers
  • Standards
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

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