Abstract
This chapter discusses the basic ideas of discrimination. It also discusses quasi-linear discrimination with only accidental parameters, the nonexistence of quasi-linear discrimination with constant errors, a general setup for discrimination, the use of maximum likelihood estimation, the discrimination between two normal populations, and the discrimination for Gumbel populations. The chapter illustrates the relationship between the common misclassification error and the Kolmogoroff distance between two densities. It also presents the generalization of the Neyman–Pearson theorem.
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