The purpose of this study is to investigate the usefulness of statistical decomposition measures in the prediction of industrial bond rating changes. Further, the predictive ability of decomposition measures is compared with multiple discriminant analysis on the same sample. The problem of this study is twofold. It stems in general from the statistical problems associated with current techniques employed in the study of bond ratings and in particular from the lack of attention to the study of bond rating changes. Two main hypotheses are tested in this study. The first is that bond rating changes can be predicted through the use of financial statement data. The second is that decomposition analysis can achieve the same performance as multiple discriminant analysis in duplicating and predicting industrial bond rating changes. To explain and predict industrial bond rating changes, statistical decomposition measures were computed for each company in the sample. Based on these decomposition measures, the two types of analyses performed were (a) a univariate analysis where each decomposition measure was compared with an industry average decomposition measure, and (b) a multivariate analysis where decomposition measures were used as independent variables in a probability linear model. In addition to statistical decomposition analysis, multiple discriminant analysis was used in duplicating and predicting bond rating changes. Finally, a comparison was made between the predictive abilities of decomposition analysis and discriminant analysis.

京都大学 / 0048 / 新制・課程博士 / 博士(医科学) / 甲第22378号 / 医科博第108号 / 新制||医科||7(附属図書館) / 京都大学大学院医学研究科医科学専攻 / (主査)教授 萩原 正敏, 教授 羽賀 博典, 教授 松村 由美 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

This dissertation studied the problem of face recognition when facial images have partial occlusions like sunglasses and scarfs. These partial occlusions lead to the loss of discriminatory information when trying to recognise a person's face using traditional face recognition techniques that do not take into account these shortcomings. This dissertation aimed to fill the gap of knowledge. Several papers in literature put forward the theory that not all regions of the face contribute equally when discriminating between different subjects. They state that some regions of the face are more equal than others, like the eyes and nose. While this may be true in theory there was a need to comprehensively study this problem. A weighting technique was introduced that that took into account the different features of the face and assigned weights for the different features of the face based on their distance from the five points that were identified as the centre of the weighing technique. Five centres were chosen which were the left eye, the right eye, the centre of the brows, the nose and the mouth. These centres perfectly captured were the five dominant regions of the face where roughly located. This weighing technique was fused with an image segmentation process that ultimately led to a hybrid approach to face recognition. Five features of the face were identified and studied quantitatively on how much they influence face recognition. These five features were the chin (C), eyes (E), forehead (F), mouth (M) and finally the nose (N). For the system to be robust and thorough, combinations of these five features were constructed to make 31 models that were used for both training and testing purposes. This meant that each of the five features had 16 models associated with it. For example, the chin (C) had the following models associated with it; C, CE, CF, CM, CN, CE, CEM, CEN, CFM, CFN, CMN, CEFM CEFN, CEMN, CFMN and CEFMN. These models were put in five different groupings called Category 1 up to Category 5. A Category 3 model implied that only three out of the five features were utilised for training the algorithm and testing. An example of a Category 3 model was the CFN model. This meant that this model simulated partial occlusion on the mouth and the chin region. The face recognition algorithm was trained on all these different models in order to ascertain the efficiency and effectiveness of this proposed technique. The results were then compared with various methods from the literature. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng (Computer Engineering) / Unrestricted

The thesis deals with selected classification methods. The thesis describes the basis of cluster analysis, discriminant analysis and theory of classification trees. The usage is demonstrated by classification of simulated data, the calculation is made in the program STATISTICA. In practical part of the thesis there is the comparison of the methods for classification of real data files of various extent. Classification methods are used for solving of the real task – prediction of air pollution based of the weather forecast.

Please read the abstract (Summary) in the 00front part of this document / Dissertation (MA (HMS))--University of Pretoria, 2002. / Biokinetics, Sport and Leisure Sciences / unrestricted

This licentiate consists of two papers treating polynomial sequences defined by linear recurrences. In paper I, we establish necessary and sufficient conditions for the reality of all the zeros in a polynomial sequence {P_i} generated by a three-term recurrence relation P_i(x)+ Q_1(x)P_{i-1}(x) +Q_2(x) P_{i-2}(x)=0 with the standard initial conditions P_{0}(x)=1, P_{-1}(x)=0, where Q_1(x) and Q_2(x) are arbitrary real polynomials. In paper II, we study the root distribution of a sequence of polynomials {P_n(z)} with the rational generating function \sum_{n=0}^{\infty} P_n(z)t^n= \frac{1}{1+ B(z)t^\ell +A(z)t^k} for (k,\ell)=(3,2) and (4,3) where A(z) and B(z) are arbitrary polynomials in z with complex coefficients. We show that the roots of P_n(z) which satisfy A(z)B(z)\neq 0 lie on a real algebraic curve which we describe explicitly.

In this thesis, the performance of the classification methods Linear Discriminant Analysis (LDA), Random Forests (RF), and Support Vector Machines (SVM) are compared using procurement data to predict what size company will win a procurement. This is useful information for companies, since bidding on a procurement takes time and resources, which they can save if they know their chances of winning are low. The data used in the models are collected from OpenTender and allabolag.se and represent procurements that were awarded to companies in 2020. A total of 8 models are created, two versions of the LDA model, two versions of the RF model, and four versions of the SVM model, where some models are more complex than others. All models are evaluated on overall performance using hit rate, Huberty’s I Index, mean average error, and Area Under the Curve. The most complex SVM model performed the best across all evaluation measurements, whereas the less complex LDA model performed overall worst. Hit rates and mean average errors are also calculated within each class, and the complex SVM models performed best on all company sizes, except the small companies which were best predicted by the less complex Random Forest model.

The distinct ecological requirements of Microtus xanthognathus (yellow-cheeked vole or taiga vole) and M. pennsylvanicus (meadow vole) warrant accurate discrimination of their remains in studies of paleoecology and past biogeographical shifts. An occlusal length of the lower 1st molars (ml) that is >3.2 mm for M. xanthognathus is the method most frequently used to separate these 2 taxa in archaeological and paleontological samples. However, these measurements alone are unreliable because some specimens of M. pennsylvanicus overlap smaller individuals of M. xanthognathus in size. Therefore, I created and tested a morphometric technique that discriminates Recent lower 1st molars (mis) of M. pennsylvanicus from those of M. xanthognathus, and is applicable to other taxa (both modern and fossil). Despite overlapping occlusal length, my discriminant function based on landmark data correctly classified 100% (n = 53) of Recent m1s from the 2 taxa and 97.7% (43 of 44) of (assumed) m1s of M. pennsylvanicus from an archaeological site from about AD 1200 in central Nebraska. This landmark scheme is applicable to fossil and modern Microtus worldwide. © 2006 American Society of Mammalogists.

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This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.