Arminger, G.; Enache, D.; Bonne, T. (1997), Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis, and Feedforward Networks.  Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Credit Scoring using Semiparametric Methods - Müller, Rönz (1999) (Correct)
....discriminant analysis and logistic discriminant analysis. These methods can be seen to be based on scores which depend on the explanatory variables in a predefined form (usually linear) Recent methods that allow a more flexible modeling are neural networks and classification trees (see e.g. Arminger, Enache Bonne, 1997) as well as nonparametric approaches (see e.g. Henley Hand, 1996) In the following sections we discuss for real credit scoring data, how the given explanatory variables influence credit worthiness. The following Section 2 gives a short data description. Section 3 presents the results of a ....
Arminger, G.; Enache, D.; Bonne, T. (1997), Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis, and Feedforward Networks.
Classification of Binary Data with one Low-Frequency Class using.. - Enache Self-citation (Enache) (Correct)
....West 4 th Street, New York, NY 10012 denache stern.nyu.edu Working Paper RC 0001 Copyright 1998 by Daniel Enache Daniel Enache (1998) Classification of Binary Data with one Low Frequency. Working Paper RC 0001. 1 1 Introduction and Notation The sample used in the credit risk model in Arminger, Enache, and Bonne (1997) is not representative for the population. The probability p of a good credit (class 1 ) in the population lies between 93 and 99 . If a representative sample had been taken to estimate a statistical model, like an artificial neural network or a logistic discrimination model, it is very ....
Arminger, G., Enache, D., and Bonne, T. (1997), "Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis, and Feedforward Networks", Computational Statistics, Vol. 12, No. 2, 293--310.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC