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Discriminant Analysis

In: Multivariate Statistics

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin, C.A.S.E. Centre f. Appl. Stat. & Econ. School of Business and Economics)

  • Zdeněk Hlávka

    (Charles University in Prague, Faculty of Mathematics and Physics Department of Statistics)

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).

Suggested Citation

  • Wolfgang Karl Härdle & Zdeněk Hlávka, 2015. "Discriminant Analysis," Springer Books, in: Multivariate Statistics, edition 2, chapter 0, pages 245-258, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-36005-3_14
    DOI: 10.1007/978-3-642-36005-3_14
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