An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default
Download full text from publisher
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Sariev, Eduard & Germano, Guido, 2020. "Bayesian regularized artificial neural networks for the estimation of the probability of default," LSE Research Online Documents on Economics 101029, London School of Economics and Political Science, LSE Library.
More about this item
Keywordsdefault risk; logistic regression; support vector machines; ES/ K002309/1;
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-CMP-2019-03-11 (Computational Economics)
- NEP-ECM-2019-03-11 (Econometrics)
- NEP-RMG-2019-03-11 (Risk Management)
- NEP-TRA-2019-03-11 (Transition Economics)
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:100211. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (LSERO Manager). General contact details of provider: http://edirc.repec.org/data/lsepsuk.html .
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.