The new robust conic GPLM method with an application to finance: prediction of credit default

DOI10.1007/s10898-012-9902-7zbMath1273.62251OpenAlexW2034774757MaRDI QIDQ2392775

Ozlem Defterli, Zehra Çavuşoğlu, Gerhard-Wilhelm Weber, Ayşe Özmen

Publication date: 2 August 2013

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-012-9902-7

zbMATH Keywords

uncertaintyrobust optimizationpredicting default probabilitiesRCMARSrobust conic generalized partial linear model

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Linear inference, regression (62J99) Credit risk (91G40)

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Uses Software

• car
• alr3
• CMARS

Cites Work

• Unnamed Item
• CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization
• On the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization
• RCMARS: robustification of CMARS with different scenarios under polyhedral uncertainty set
• Robust optimization-methodology and applications
• Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
• Lectures on Modern Convex Optimization
• Robust Convex Optimization
• Predicting default probabilities in emerging markets by new conic generalized partial linear models and their optimization

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