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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2007). Discriminant Analysis. In: Multivariate Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-73508-5_12
Download citation
DOI: https://doi.org/10.1007/978-0-387-73508-5_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-70784-6
Online ISBN: 978-0-387-73508-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)