Efficient Estimation of Parameters for Non-Gaussian Autoregressive Processes.

Abstract

The problem of estimating the parameters of a non-Gaussian autoregressive process is addressed. Departure of the driving noise from Gaussianity is shown to have the potential of improving the accuracy of the estimation of the parameters. While the standard linear prediction techniques are computationally efficient, they show a substantial loss of efficiency when applied to non-Gaussian processes. A maximum likelihood estimator is proposed for more precise estimation of the parameters of these processes coupled with a realistic non-Gaussian model for the driving noise. The performance is compared to that of the linear prediction estimator and as expected the maximum likelihood estimator displays a marked improvement.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1986
Accession Number
ADA170978

Entities

People

  • Debasis Sengupta
  • Steven Kay

Organizations

  • University of Rhode Island

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Electrical Engineering
  • Estimators
  • Frequency
  • Gaussian Processes
  • Information Science
  • Military Research
  • New York
  • Probability
  • Probability Density Functions
  • Random Variables
  • Signal Processing
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Test And Evaluation

Fields of Study

  • Engineering

Readers

  • Statistical inference.