Order Estimation and Discrimination Between Stationary and Time-Varying (TVAR) Autoregressive Models

  • Authors:

  • Y.I. Abramovich;N.K. Spencer;M.D.E. Turley

  • Affiliations:

  • Intelligence, Surveillance, & Reconnaissance Div., Defence Sci. & Technol. Organ., Adelaide, SA;-;-

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2007

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Abstract

For a set of T independent observations of the same N-variate correlated Gaussian process, we derive a method of estimating the order of an autoregressive (AR) model of this process, regardless of its stationary or time-varying nature. We also derive a test to discriminate between stationary AR models of order m,AR(m), and time-varying autoregressive models of order m,TVAR(m). We demonstrate that within this technique the number T of independent identically distributed data samples required for order estimation and discrimination just exceeds the maximum possible order mmax, which in many cases is significantly fewer than the dimension of the problem N