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Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>1995
<mark>Journal</mark>Journal of the Royal Statistical Society: Series C (Applied Statistics)
Issue number1
Number of pages15
Pages (from-to)101-115
Publication StatusPublished
<mark>Original language</mark>English


Currently popular techniques such as experimental spectroscopy and computer-aided molecular modelling lead to data having very many variables observed on each of relatively few individuals. A common objective is discrimination between two or more groups, but the direct application of standard discriminant methodology fails because of singularity of covariance matrices. The problem has been circumvented in the past by prior selection of a few transformed variables, using either principal component analysis or partial least squares. Although such selection ensures non-singularity of matrices, the decision process is arbitrary and valuable information on group structure may be lost. We therefore consider some ways of estimating linear discriminant functions without such prior selection. Several spectroscopic data sets are analysed with each method, and questions of bias of assessment procedures are investigated. All proposed methods seem worthy of consideration in practice.