A Toeplitz Gram-Schmidt Algorithm for Autoregressive Modeling.

Abstract

An algorithm is presented for efficiently finding the information needed in the modified Gram-Schmidt decomposition of the augmented autoregressive design matrix to find the Yule-Walker estimators of autoregressive parameters for orders 1, 2, ..., M. the algorithm is shown to be slightly slower than Levinson's algorithm and to require slightly more storage but enjoys the superior numerical properties of the modified Gram-Schmidt decomposition algorithm. Further, it is shown how the algorithm provides a unified framework for suggesting robust autoregressive estimators, for finding autoregression diagnostics, and for understanding the Burg algorithm. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1982
Accession Number
ADA117071

Entities

People

  • H. Joseph Newton
  • Herbert T. Davis
  • Marcello Pagano

Organizations

  • Texas A&M University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Coefficients
  • Data Science
  • Decomposition
  • Equations
  • Errors
  • Estimators
  • Information Processing
  • Information Science
  • New York
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Estimation
  • Statistics
  • Theorems
  • Universities

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Statistical inference.