Discriminant Analysis

Wolfgang Härdle () and Leopold Simar
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Wolfgang Härdle: Humboldt-Universität zu Berlin, CASE — Center for Applied Statistics and Economics, Institut für Statistik und Ökonometrie

Chapter 12 in Applied Multivariate Statistical Analysis, 2003, pp 323-340 from Springer

Abstract: 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 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”.

Keywords: Discriminant Analysis; Linear Discriminant Analysis; Mahalanobis Distance; Allocation Rule; Bank Note (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/978-3-662-05802-2_12

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