Predicting default probabilities in emerging markets by new conic generalized partial linear models and their optimization

DOI10.1080/02331934.2011.654343zbMath1245.91080OpenAlexW2075505197MaRDI QIDQ2903133

Zehra Çavuşoğlu, Ayşe Özmen, Gerhard-Wilhelm Weber

Publication date: 23 August 2012

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331934.2011.654343

Mathematics Subject Classification ID

Economic time series analysis (91B84) Economic models of real-world systems (e.g., electricity markets, etc.) (91B74)

Related Items (7)

The new robust conic GPLM method with an application to finance: prediction of credit default ⋮ Investigating the effects of illiquidity on credit risks via new liquidity augmented stochastic volatility jump diffusion model ⋮ Sparse regression modeling for short- and long-term natural gas demand prediction ⋮ Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market ⋮ MARS as an alternative approach of Gaussian graphical model for biochemical networks ⋮ Credit Risk Management Using Automatic Machine Learning ⋮ Vine copula graphical models in the construction of biological networks

Uses Software

• CMARS

Cites Work

• 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
• Multivariate adaptive regression splines

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