Preface.
1. General Introduction.
2. Likelihood-Based Approaches to Discrimination.
3. Discrimination via Normal Models.
4. Distributional Results for Discrimination via Normal Models.
5. Some Practical Aspects and Variants of Normal Theory-Based Discriminant Rules.
6. Data Analytic Considerations with Normal Theory-Based Discriminant Analysis.
7. Parametric Discrimination via Nonnormal Models.
8. Logistic Discrimination.
9. Nonparametric Discrimination.
10. Estimation of Error Rates.
11. Assessing the Reliability of the Estimated Posterior Probabilities of Group Membership.
12. Selection of Feature Variables in Discriminan Analysis.
13. Statistical Image Analysis.
References.
Author Index.
Subject Index.
Geoffrey J. McLachlan, PhD, is Professor of Mathematics at the University of Queensland, Australia. He is the author, with David Peel, of Finite Mixture Models(Wiley) and, with Thriyambakam Krishnan, of The EM Algorithm and Extensions(Wiley), among others.
“ … in my opinion (this book) has been proved .. to be a valuable resource (and) should not be overlooked by any scholarly library.” (Journal of the Royal Statistical Society Series A, June 2005)
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