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.

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