Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Covariance Characterization by Partial Autocorrelation Matrices

M. Morf, A. Vieira and T. Kailath
The Annals of Statistics
Vol. 6, No. 3 (May, 1978), pp. 643-648
Stable URL: http://www.jstor.org/stable/2958567
Page Count: 6
  • Read Online (Free)
  • Download ($19.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Covariance Characterization by Partial Autocorrelation Matrices
Preview not available

Abstract

It is known that the autocorrelation function of a stationary discrete-time scalar process can be uniquely characterized by the so-called partial autocorrelation function, which is a sequence of numbers less or equal to one in magnitude. We show here that the matrix covariance function of a multivariate stationary process can be characterized by a sequence of matrix partial correlations, having singular values less than or equal to one in magnitude. This characterization can be used to extend to the multivariate case the so-called maximum entropy spectral analysis method.

Page Thumbnails

  • Thumbnail: Page 
643
    643
  • Thumbnail: Page 
644
    644
  • Thumbnail: Page 
645
    645
  • Thumbnail: Page 
646
    646
  • Thumbnail: Page 
647
    647
  • Thumbnail: Page 
648
    648