Multidimensional Probability Density Function Approximations for Detection, Classification, and Model Order Selection

Abstract

This paper addresses the problem of calculating the multidimensional probability density functions (PDFs) of statistics derived from known many-to-one transformations of independent random variables (RVs) with known distributions. The statistics covered in the paper include reflection coefficients, autocorrelation estimates, cepstral coefficients, and general linear functions of independent RVs. Through PDF transformation, these results can be used for general PDF approximation, detection, classification, and model order selection. A model order selection example that shows significantly better performance than the Akaike and MDL method is included.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2001
Accession Number
ADA477213

Entities

People

  • Albert H. Nuttall
  • Paul Baggenstoss
  • Steven Kay

Organizations

  • University of Rhode Island

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Classification
  • Data Science
  • Detection
  • Estimators
  • Fourier Analysis
  • Gaussian Noise
  • Information Science
  • Mathematical Analysis
  • Order Statistics
  • Probability
  • Probability Density Functions
  • Random Variables
  • Signal Processing
  • Statistical Analysis
  • Statistics
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Statistical inference.