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Discriminant and cluster analysis as a tool for classification of objects
Rynešová, Pavlína ; Löster, Tomáš (advisor) ; Řezanková, Hana (referee)
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can be a disordered group of objects organized into several internally homogeneous classes or clusters. Discriminant analysis creates knowledge based on the jurisdiction of existing classes classification rule, which can be then used for classifying units with an unknown group membership. The aim of this thesis is a comparison of discriminant analysis and different methods of cluster analysis. To reflect the distances between objects within each cluster, squeared Euclidean and Mahalanobis distances are used. In total, there are 28 datasets analyzed in this thesis. In case of leaving correlated variables in the set and applying squared Euclidean distance, Ward´s method classified objects into clusters the most successfully (42,0 %). After changing metrics on the Mahalanobis distance, the most successful method has become the furthest neighbor method (37,5 %). After removing highly correlated variables and applying methods with Euclidean metric, Ward´s method was again the most successful in classification of objects (42,0%). From the result implies that cluster analysis is more precise when excluding correlated variables than when leaving them in a dataset. The average result of discriminant analysis for data with correlated variables and also without correlated variables is 88,7 %.
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Building a predictive model for bankruptcy
BÜRGER, Pavel
Thesis deals with complex process of creation of new bankruptcy model for predicting business failure, while this process involves selection of quality sample, verification of classification accuracy of already existing bankruptcy models, profile analysis and finally the derivation of specific equation of bankruptcy model. The derivation is performed by using two selected statistical methods, discriminant analysis and logistic regression. Two bankruptcy models Bürger's index DA12 and Bürger's index LR12 were derived by using the mentioned statistical methods. The new models distinct advantage is, unlike already existing and renowned bankruptcy models, that they are focused on classification of micro and small enterprises in terms of Czech Republic, while classification accuracy one year before failure is by individual models 74.8 % and 81.87 %. Derived models have clear interpretation (no grey zone) and easy calculation, which brings a possibility for micro and small entrepreneurs to check their business partners in terms of failure prediction.
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Financial distress prediction of company
MAŇASOVÁ, Helena
The theoretical part of this master thesis deals with creation and solution of financial distress and analysing classification models. In the practical part I defined own methods for financial distress prediction of company using discriminant analysis and logistic regression.
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Analysis of determinants of company’s performance
Jankovská, Petra ; Neumaierová, Inka (advisor) ; Hájek, Jiří (referee)
The master's thesis aim is to construct an index of profitability of Czech banks using the method of discriminant analysis. The bases for construction of the index are commonly used ratios characteristic for the banking sector whose impact on profitability was evaluated as statistically significant. These indicators are calculated for a sample of 16 banks over three years; the individual observations were classified into one of two groups according to profitability. Besides the construction of the index and the subsequent description of the results, the work also deals with a brief description of the Czech banking sector and its development during the reference period. The theoretical part is mainly focused on description of the specifics of bank statements and interpretation of financial ratios used in the model. One chapter also discuss the basis of discriminant analysis.
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The genus Puccinelia (Parl.) in Western Himalaya
KARÁSEK, Jakub
The aim of this study was found the best characters for determination of six Puccinellia species growing in western Himalaya. The sets of quantitative and qualitative were examined. The best characters for species determination were Lemma apex, glume apex, anthers length. New character the structure of cross section of leaf was examined and found to be good for species determination.
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