Publisher’s description: Discriminant analysis or (statistical) discrimination has proven indispensable to fields as diverse as the physical, biological and social sciences, engineering and medicine. This comprehensive text provides perhaps the first truly modern, comprehensive and systematic account of discriminant analysis and statistical pattern recognition with an emphasis on the field’s key recent advances. Contents: General introduction; Likelihood-based approaches to discrimination; Discrimination via normal model; Distributional results for discrimination via normal models; Some practical aspects and variants of normal theory-based discriminant rules; Data analytic considerations with normal theory-based discriminant analysis; Parametric discrimination via nonnormal models; Logistic discrimination; Nonparametric discrimination; Estimation of error rates; Assessing the reliability of the estimated posterior probability of group membership; Selection of feature variables in discriminant analysis; Statistical image analysis; References; Indices.