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The problem of classification is an old one that has
application in geophysical, medical, signal
processing and many other fields. There are numerous
approaches to this problem using the statistical
properties of the populations from which observations
are drawn. In applications such as geophysical and
signals processing there is a natural structure on
the variance-covariance matrix of the observation
vectors. The efficacy of classification is generally
increased by taking that structure into account. One
such structure that is used to model that
variance-covariance matrix is the autoregressive
circulant (ARC) structure. In this book,
classification rules are derived using the assumption
of such a structure. Techniques to compute these
rules are discussed and their efficacy studied.
application in geophysical, medical, signal
processing and many other fields. There are numerous
approaches to this problem using the statistical
properties of the populations from which observations
are drawn. In applications such as geophysical and
signals processing there is a natural structure on
the variance-covariance matrix of the observation
vectors. The efficacy of classification is generally
increased by taking that structure into account. One
such structure that is used to model that
variance-covariance matrix is the autoregressive
circulant (ARC) structure. In this book,
classification rules are derived using the assumption
of such a structure. Techniques to compute these
rules are discussed and their efficacy studied.