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Discriminant Analysis

In: Applied Multivariate Statistical Analysis

Author

Listed:
  • Wolfgang Härdle

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

  • Léopold Simar

    (Université Catholique Louvain, Inst. Statistique)

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”.

Suggested Citation

  • Wolfgang Härdle & Léopold Simar, 2003. "Discriminant Analysis," Springer Books, in: Applied Multivariate Statistical Analysis, chapter 12, pages 323-340, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-05802-2_12
    DOI: 10.1007/978-3-662-05802-2_12
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