High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 2. A New Technique for Distribution Approximation of Random Data.

Abstract

This thesis deals with the analysis of random data. Two approaches are discussed. The first approach is a Goodness of Fit test to determine whether or not random data samples are statistically consistent with a prespecified probability distribution. The well known Kolmogorov Smirnov test, Chi Square test, Q-Q Plots and P-P plots are reviewed and illustrated by means of several examples. A new algorithm, the Ozturk Algorithm, is introduced. The second approach deals with approximation of the underlying probability density function of random data samples. The previously mentioned well known tests are not suitable for this task. However, the Ozturk Algorithm provides a powerful solution for this problem with a nice graphical interpretation. Finally, computer simulated results obtained with the Ozturk Algorithm are presented and discussed.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1995
Accession Number
ADA300902

Entities

People

  • Rajiv R. Shah

Organizations

  • University of Massachusetts Amherst

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Chi Square Test
  • Data Science
  • Goodness Of Fit Tests
  • Information Processing
  • Information Science
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Signal Processing
  • Statistical Algorithms

Readers

  • Computer Vision.
  • Statistical inference.