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 xi 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”.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(2007). Discriminant Analysis. In: Applied Multivariate Statistical Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72244-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-72244-1_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72243-4
Online ISBN: 978-3-540-72244-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)