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Published in:
2012 | OriginalPaper | Chapter
Discriminant Analysis
Authors : Wolfgang Karl Härdle, Léopold Simar
Published in: Applied Multivariate Statistical Analysis
Publisher: Springer Berlin Heidelberg
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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 showed difficulties in repaying their loan). When a new customer asks for a loan, the bank has to decide whether or not to give the loan. The past records of the bank provides two data sets: multivariate observations
x
i
on the two categories of customers (including for example age, salary, marital status, the amount of the loan, etc.). The new customer is a new observation
x
with the same variables. The discrimination rule has to classify the customer into one of the two existing groups and the discriminant analysis should evaluate the risk of a possible “bad decision”.