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Higher Order Statistics

  • Thierry Chonavel
Part of the Advanced Textbooks in Control and Signal Processing book series (C&SP)

Abstract

Purpose In this chapter, we present a few basic notions about higher order statistics that generalise notions defined above for second order statistics. We show, in particular, how the definition of the spectral representation of a process can be generalised for processes that are stationary at orders higher than two.

Keywords

ARMA Model High Order Statistics Cumulant Function Autocovariance Function Inverse Filter 
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Notes

  1. 1.
    The Kronecker product of two matrices A and B of respective size m a x n a and m b x n b is the matrix AB of size m a m b x n a n b, with general term [A ⊗ B]ima+k,jna+l = [A]ij[B]kl.Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Thierry Chonavel
    • 1
  1. 1.ENST de BretagneTechnopôle de Brest IroiseBrest CedexFrance

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