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Multivariate kernel discrimination applied to bank loan classification

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Τίτλος: Multivariate kernel discrimination applied to bank loan classification
Συγγραφείς: Caruana, Mark Anthony, Lentini, Gabriele
Στοιχεία εκδότη: John Wiley & Sons, Inc.
Έτος έκδοσης: 2024
Συλλογή: University of Malta: OAR@UM / L-Università ta' Malta
Θεματικοί όροι: Kernel functions, Bank loans -- Statistical methods, Discriminant analysis -- Mathematical models, Multivariate analysis -- Data processing, Banks and banking -- Malta, Central Bank of Malta
Περιγραφή: The purpose of this paper is to apply a kernel discriminant analysis to classify bank loans and determine which loans are at risk of default. This study starts by introducing the concept of kernel density estimation, which is a widely used non-parametric technique to obtain an estimate for the probability density function. This procedure is based on two main parameters: the kernel function and the bandwidth, the latter being the crucial parameter. The multivariate kernel density estimator is later applied to discriminant analysis to obtain kernel discrimination. This is a method which classifies observations into a predetermined number of distinct and disjoint classes. Finally, we apply multivariate kernel discriminant analysis to a sample of bank loans to determine which loans can be classified as defaulted. This model can help predict the likelihood that future loans may default. ; peer-reviewed
Τύπος εγγράφου: book part
Γλώσσα: English
ISBN: 978-1-78630-962-4
1-78630-962-9
Relation: https://www.um.edu.mt/library/oar/handle/123456789/121043
DOI: 10.1002/9781394284061.ch2
Διαθεσιμότητα: https://www.um.edu.mt/library/oar/handle/123456789/121043
https://doi.org/10.1002/9781394284061.ch2
Rights: info:eu-repo/semantics/closedAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Αριθμός Καταχώρησης: edsbas.6BAEAEB
Βάση Δεδομένων: BASE
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  Data: Multivariate kernel discrimination applied to bank loan classification
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Caruana%2C+Mark+Anthony%22">Caruana, Mark Anthony</searchLink><br /><searchLink fieldCode="AR" term="%22Lentini%2C+Gabriele%22">Lentini, Gabriele</searchLink>
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  Data: John Wiley & Sons, Inc.
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  Data: 2024
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  Data: University of Malta: OAR@UM / L-Università ta' Malta
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  Data: <searchLink fieldCode="DE" term="%22Kernel+functions%22">Kernel functions</searchLink><br /><searchLink fieldCode="DE" term="%22Bank+loans+--+Statistical+methods%22">Bank loans -- Statistical methods</searchLink><br /><searchLink fieldCode="DE" term="%22Discriminant+analysis+--+Mathematical+models%22">Discriminant analysis -- Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis+--+Data+processing%22">Multivariate analysis -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Banks+and+banking+--+Malta%22">Banks and banking -- Malta</searchLink><br /><searchLink fieldCode="DE" term="%22Central+Bank+of+Malta%22">Central Bank of Malta</searchLink>
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  Data: The purpose of this paper is to apply a kernel discriminant analysis to classify bank loans and determine which loans are at risk of default. This study starts by introducing the concept of kernel density estimation, which is a widely used non-parametric technique to obtain an estimate for the probability density function. This procedure is based on two main parameters: the kernel function and the bandwidth, the latter being the crucial parameter. The multivariate kernel density estimator is later applied to discriminant analysis to obtain kernel discrimination. This is a method which classifies observations into a predetermined number of distinct and disjoint classes. Finally, we apply multivariate kernel discriminant analysis to a sample of bank loans to determine which loans can be classified as defaulted. This model can help predict the likelihood that future loans may default. ; peer-reviewed
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  Data: info:eu-repo/semantics/closedAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
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        Value: 10.1002/9781394284061.ch2
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      – Text: English
    Subjects:
      – SubjectFull: Kernel functions
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      – SubjectFull: Bank loans -- Statistical methods
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      – SubjectFull: Discriminant analysis -- Mathematical models
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      – SubjectFull: Multivariate analysis -- Data processing
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      – SubjectFull: Banks and banking -- Malta
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      – TitleFull: Multivariate kernel discrimination applied to bank loan classification
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              Y: 2024
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