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Scoring Models in Finance (Skóringové modely ve financích)
Rychnovský, Michal ; Zouhar, Jan (advisor) ; Kalčevová, Jana (referee)
The aim of the present work is to describe the application of the logistic regression model to the field of probability of default modeling, and provide a brief introduction to the scoring development process used in financial practice. We start by introducing the theoretical background of the logistic regression model; followed by a consequent derivation of three most common scoring models. Then we present a formal definition of the Gini coefficient as a diversification power measure and derive the Somers-type formulas for its estimation. Finally, the key part of this work gives an overview of the whole scoring development process illustrated on the examples of real business data.
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Robustnost mediánového odhadu v Bernoulliově logistické regresi
Hobza, Tomáš ; Pardo, L.
In the paper the median estimator of the logistic regression parameters employing smoothed data in the discrete case is considered. Sensitivity of this estimator to contaminations of the logistic regression data is studied by simulations and compared with the sensitivity of some robust estimators previously introduced to logistic regression.
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Comparison of approaches to creating credit scoring models
Hofman, Elena ; Šedivý, Jan (advisor)
This work is focused on the management of a credit risk related to the traditional bank lending business to individuals. The paper deals with a theory of measuring risk with help of PD (Probability of Default) parameter when different scoring models are used. The goal is to outline an issue with the credit risk and its management in general, attention is paid to details of a process of creating scoring models. There are three specific modeling techniques listed, namely logistic regression, decision trees and neural networks. Methods are explained in detail and are given possibilities of mutual comparison. The application part is devoted to the evaluation and comparison of credit scoring models based on these methods.
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Comparison of the potency of application KDD methods and statistical methods in the analysis of ADAMEK data
Líbal, Petr ; Rauch, Jan (advisor) ; Berka, Petr (referee)
This bachelor thesis compares association rules and logistic regression. For this comparison medical data Adamek have been used. The relationship between attributes belonging to a group of Physical examinations and Difficulty has been studied. Both methods are theoretically described, their connection with the related common areas is mentioned - the analysis of market basket in the case of association rules, linear regression in the case of logistic regression. Before the analysis attributes are described with basic statistics and the distribution of values is graphically illustrated. In both cases, analysis proceed the same way. First, the relationship of each difficulty is examined, then is examined relationship of difficulties in general. In conclusion, the results of both methods is compared.
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