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asymptotic distribution, we also obtain robustness results for our estimator. All of our results are valid for a broad class of ß …
Persistent link: https://www.econbiz.de/10010310510
banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary … and probit models as benchmark On overall, GA-SVM is outperforms compared to the benchmark models in both training and …
Persistent link: https://www.econbiz.de/10010318756
Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of … accuracy the SVM has a lower model risk than the Logit on average and displays a more robust performance. This result holds …
Persistent link: https://www.econbiz.de/10010281539
Persistent link: https://www.econbiz.de/10015044842
The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness …. Kernel logistic regression, support vector machines, least squares and the AdaBoost loss function are treated as special …
Persistent link: https://www.econbiz.de/10010477496
nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. …
Persistent link: https://www.econbiz.de/10010516923
nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. …
Persistent link: https://www.econbiz.de/10010306241
The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness …. Kernel logistic regression, support vector machines, least squares and the AdaBoost loss function are treated as special …
Persistent link: https://www.econbiz.de/10010306271
We consider the component analysis problem for a regression model with an additive structure. The problem is to check the hypothesis of linearity for each component without specifying the structure of the remaining components. In this paper we show that under mild conditions on the design and...
Persistent link: https://www.econbiz.de/10010310801
Additive models of the type y=f_1(x_1)+...+f_p(x_p)+e where f_j,j=1,...,p, have unspecified functional form, are flexible statistical regression models which can be used to characterize nonlinear regression effects. The basic tools used for fitting the additive model are the expansion in...
Persistent link: https://www.econbiz.de/10010265642