Abstract
Discriminant analysis is used in situations where the clusters are known a priori. The aim of discriminant analysis is to classify an observation, or several observations, into these known groups. For instance, in credit scoring, a bank knows from past experience that there are good customers (who repay their loan without any problems) and bad customers (who have had difficulties repaying their loans). When a new customer asks for a loan, the bank has to decide whether or not to give the loan. The information of the bank is given in two data sets: multivariate observations on the two categories of customers (including age, salary, marital status, the amount of the loan, and the like).
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References
Härdle, W., & Simar, L. (2015). Applied multivariate statistical analysis (4th ed.). Berlin: Springer.
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Härdle, W.K., Hlávka, Z. (2015). Discriminant Analysis. In: Multivariate Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36005-3_14
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DOI: https://doi.org/10.1007/978-3-642-36005-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36004-6
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