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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
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
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
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
This article proposes a simple and fast approach to build simultaneous confi dence bands and perform specification tests for smooth curves in additive models. The method allows for handling of spatially heterogeneous functions and its derivatives as well as heteroscedasticity in the data. It is...
Persistent link: https://www.econbiz.de/10010329955
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
In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use separate toolsto solve the different tasks of variable selection and smoothing parameter choice. The...
Persistent link: https://www.econbiz.de/10010266175
This paper explores the properties of using a generalized additive model with embedded variable selection for the prediction of bankruptcy. The main purpose is to explore an innovative way to close the gap between interpretation and prediction that has prevented widespread use of methods based...
Persistent link: https://www.econbiz.de/10012657511
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