Abstract
This chapter discusses discrimination and allocation. Regression data are commonly the result of sampling a population, taking two or more measurements on each individual sampled, and then examining how those variables relate to one another. Discrimination problems have a very different sampling scheme. In discrimination problems data are obtained from multiple groups and we seek efficient means of telling the groups apart, i.e., discriminating between them. Discrimination is closely related to one-way multivariate analysis of variance in that we seek to tell groups apart. One-way multivariate analysis of variance addresses the question of whether the groups are different whereas discriminant analysis seeks to specify how the groups are different. Allocation is the problem of assigning new individuals to their appropriate group. Allocation procedures have immediate application to diagnosing medical conditions.
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Christensen, R. (2019). Discrimination and Allocation. In: Advanced Linear Modeling. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-29164-8_12
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