Boundary Kernel Estimation of the Two Sample Comparison Density Function

Abstract

The focus of this work is to derive functional and grap hical statistical techniques for the two sample problem suitable for implementation in modern computing environments. In the two sample problem, it is desired to test the null hypothesis that two independent random samples have a common distribution function. Assuming certain conditions on the distribution functions, a procedure is proposed which has strong graphical elements, a sound theoretical foundation, and estimates the relation of the two distributions if the null hypothesis is rejected. The proposed procedure has as its motivation the estimation of the comparison density and inference concerning its uniformity.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1989
Accession Number
ADA210757

Entities

People

  • William P. Alexander

Organizations

  • Texas A&M University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Chi Square Test
  • Data Mining
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Integral Equations
  • Normal Distribution
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Samples
  • Statistical Tests
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Instructional Design and Training Evaluation.
  • Regression Analysis.

Technology Areas

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