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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Johnson, R. A., & Wichern, D. W. (1998). Applied multivariate analysis (4th ed.). Englewood Cliffs, NJ: Prentice Hall.
Lachenbruch, P. A., & Mickey, M. R. (1968). Estimation of error rates in discriminant analysis. Technometrics, 10, 1–11.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Härdle, W.K., Simar, L. (2015). Discriminant Analysis. In: Applied Multivariate Statistical Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45171-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-662-45171-7_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45170-0
Online ISBN: 978-3-662-45171-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)