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Nalbantov, G.I.
2008-09-11
regularization, instance-based learning, kernel methods, econometrics and machine learning, financial time-series forecasting, binary problems in marketing
The subject of this PhD research is within the areas of Econometrics and Artificial Intelligence. More concretely, it deals with the tasks of statistical regression and classification analysis. New classification methods have been proposed, as well as new applications of established ones in the areas of Finance and Marketing. The bulk of this PhD research centers on extending standard methods that fall under the general term of loss-versus-penalty classification techniques. These techniques build on the premises that a model that uses a finite amount of available data to be trained on should neither be too complex nor too simple in order to possess a good forecasting ability. New proposed classification techniques in this area are Support Hyperplanes, Nearest Convex Hull classification and Soft Nearest Neighbor. Next to the new techniques, new applications of some standard loss-versus-penalty methods have been put forward. Specifically, these are the application of the so-called Support Vector Machines (SVMs) for classification and regression analysis to financial time series forecasting, solving the Market Share Attraction model and solving and interpreting binary classification tasks in Marketing. In addition, this research focuses on new efficient solutions to SVMs using the so-called majorization algorithm. This algorithm provides for the possibility to incorporate various so-called loss functions while solving general SVM-like methods.
Erasmus University Rotterdam (EUR) Erasmus School of Economics (ESE) Erasmus Research Institute of Management (ERIM)
Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam (ERIM is the joint researchinstitute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam)
9058921666
1566-5283
