Evaluation of the Degree of Separation between Two Data Populations with Statistical Algorithms

Abstract

This report investigates various existing statistical methods available in the literature and new statistical methods for evaluating the degree of separation between two peaks or two populations of data. The algorithms evaluated in this study include the direct percentage of overlap between the two populations of data, the Kolmogorov-Smirnov (K-S) test, the area between the receiver operating characteristic (ROC) curve and diagonal line, and the ROC curve length (LROC). These algorithms are compared to the standard reference probability distribution for each data profile to be separated. Evaluations of the algorithms are presented in order to determine the relative degree of separation between the two peaks or distributions. The LROC is determined to provide the best estimation of the degree of separation compared to the other methods.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2012
Accession Number
ADA576337

Entities

People

  • A. P. Snyder
  • Waleed M. Maswadeh

Organizations

  • Edgewood Chemical Biological Center

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  • Counter WMD

DTIC Thesaurus Topics

  • Algorithms
  • Binomials
  • Data Analysis
  • Data Science
  • Data Sets
  • Databases
  • Information Science
  • Literature
  • New York
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Square Waves
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Test And Evaluation

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Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference