The Classification of Data under Autoregressive Circulant Covariance

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.

Autorentext

Christopher Louden received his MS in Statistics from theUniversity of Texas at San Antonio. The University of TexasHealth Science Center in San Antonio currently employs him as aBiostatistician. He is an avid reader and enjoys relaxingweekends in the Texas Hill Country.

Klappentext

The problem of classification is an old one that hasapplication in geophysical, medical, signalprocessing and many other fields. There are numerousapproaches to this problem using the statisticalproperties of the populations from which observationsare drawn. In applications such as geophysical andsignals processing there is a natural structure onthe variance-covariance matrix of the observationvectors. The efficacy of classification is generallyincreased by taking that structure into account. Onesuch structure that is used to model thatvariance-covariance matrix is the autoregressivecirculant (ARC) structure. In this book,classification rules are derived using the assumptionof such a structure. Techniques to compute theserules are discussed and their efficacy studied.