1955
DOI: 10.1002/j.2333-8504.1955.tb00265.x
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Probabilities of Misclassification in Discriminatory Analysis

Abstract: The problem concerns the classification of an individual on the basis of a set of measurements into one of two populations assumed to have the multivariate normal form with the same dispersion matrix. Probabilities of misclassification predicted with the discriminant function under several problematic conditions are computed and factors associated with these probabilities are discussed. Predicted values are compared with those obtained empirically through cross‐validiation.

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“…17,18 Everyday and not-so-everyday examples include blood, paint, milk, clay, photonic crystals, shampoo, viruses, globular proteins, pharmaceuticals, and even sewage, among many others. [19][20][21][22][23][24][25][26][27][28] Therefore, understanding the impact of polydispersity on the phase behaviour of many-body systems is of fundamental, commercial, and practical interest. 29 For instance, knowing under what conditions (and how) a multicomponent fluid will demix may be essential in determining the shelf life of a product.…”
Section: Introductionmentioning
confidence: 99%
“…17,18 Everyday and not-so-everyday examples include blood, paint, milk, clay, photonic crystals, shampoo, viruses, globular proteins, pharmaceuticals, and even sewage, among many others. [19][20][21][22][23][24][25][26][27][28] Therefore, understanding the impact of polydispersity on the phase behaviour of many-body systems is of fundamental, commercial, and practical interest. 29 For instance, knowing under what conditions (and how) a multicomponent fluid will demix may be essential in determining the shelf life of a product.…”
Section: Introductionmentioning
confidence: 99%