The diploma thesis is focused on predictive modeling in credit risk management. Banks and financial institutions are mainly interested in it to estimate the probability of client's default in order to make a decision about which client will be accepted and which client will be rejected. The theoretical part includes an introduction of credit scoring and a description of discrete choice models. The linear probability model, the probit model and the logit model are described in detail. The logit model is afterwards used for the prediction of client's default. The practical part is focused on a statistical description of the dataset and a description of how to work with it before we start with the development of the credit scoring model. After that follows the estimation of the model on testing sample, its testing and the estimation of the model on full sample with a description of individual steps of calculation and outputs of the program SPSS.

The goal of this thesis is introduce the theory of generalized linear models, namely probit and logit model. This models are especially used for medical data processing. In our concrete case these mentioned models are applied to data file obtained in teaching hospital Brno. The aim is statically analyzed immune response of child patients in dependence of twelve selected types of genes and find out which combinations of these genes influence septic state of patients.

This thesis sumarizes in detail general linear regression model, including testing statistics for coefficients, submodels, predictions and mostly tests of outliers and large leverage points. It describes how to include categorial variables into regression model. This model was applied to describe saturation of photographs of bread, where input variables were, type of flour, type of addition and concntration of flour. After identification of outliers it was possible to create mathematical model with high coefficient of determination, which will be usefull for experts in food industry for preliminar identification of possible composition of bread.

Despite the alarming incidence rate and potential for debilitating outcomes of sports-related concussion, the underlying mechanisms of injury remain to be expounded. Since as early as 1950, researchers have aimed to characterize head impact biomechanics through in-lab and in-game investigations. The ever-growing body of literature within this area has supported the inherent connection between head kinematics during impact and injury outcomes. Even so, traditional metrics of peak acceleration, time window, and HIC have outlived their potential. More sophisticated analysis techniques are required to advance the understanding of concussive vs subconcussive impacts. The work presented within this thesis was motivated by the exploration of advanced approaches to 1) experimental theory and design of impact reconstructions and 2) characterization of kinematic profiles for model building. These two areas of investigation resulted in the presentation of refined, systematic approaches to head impact analysis that should begin to replace outdated standards and metrics.

This study implements radial and non-radial Data Envelopment Analysis (DEA) models to assess eco-efficiency and eco-productivity of the 50 states in the United States in 2018. The models are based on three inputs (capital stock, employment, and energy consumption), a single desirable output (real gross domestic product) and a single undesirable output variable (CO2 emissions). The radial DEA models reveal that at least 32 states are operated efficiently. Five states perform at the most optimal scale size, whereas 17 states have considerable potential to boost their productive efficiencies by enlarging available resources, and 28 states are overinvested in their input variables given their current output levels. The non-radial DEA models show that, overall, the states’ capital efficiency is very high, whereas energy and emission efficiencies are very low. The states’ eco-productivity is relatively higher than the eco-efficiency levels. In the second stage of the analysis, non-parametric statistical tests and Tobit regressions are conducted for further investigation. According to the non-parametric statistical test, high capital stock, labor force, and energy usage do not affect the states’ productive efficiency. However, states with low carbon dioxide emissions have significantly higher eco-efficiency and eco-productivity levels. The Tobit regression results illustrate that nuclear power and renewable energy consumption significantly affect the states’ relative efficiencies.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19986号 / 工博第4230号 / 新制||工||1654(附属図書館) / 33082 / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 酒井 伸一, 教授 米田 稔, 准教授 平井 康宏 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Product quality is an often-overlooked factor in the financial analysis of video games. Quality measurements have been proven to work as a reliable predictor of sales while also directly influencing performance in financial markets. If markets are efficient in reflecting new information, perception of video game quality will lead to a rational response. This thesis examines the market reaction to this information set. The release structure in the video game industry allows for a direct observation of the isolated quality effect through third-party reviews. These reviews form an objective measurement of game quality without having other revealing characteristics, as all other information is released prior to these reviews. The possibility to exploit this unique case motivates the analysis through multiple empirical designs. Results from a multivariate regression model show a statistically significant positive effect of higher quality on short-term returns over all models. The release of a lower quality game reduces returns only for high-profile games. Both of these results are confirmed by the results from a rules-based trading strategy. These effects subside in the face of longer holding periods and higher exposure. This thesis finds sufficient evidence that video game quality should be an important factor in the analysis of video game companies. At the same time, these effects are only persistent in the short-time validating an efficient response to new information by financial investors.

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In recent years the use of advanced driving simulators has increased in the transportation engineering field especially in evaluating safety countermeasures. The driving simulator at UCF is a high fidelity simulator with six degrees of freedom. This research aims at validating the simulator in terms of speed and safety with the intention of using it as a test bed for high risk locations and to use it in developing traffic safety countermeasures. The Simulator replicates a real world signalized intersection (Alafaya trail (SR-434) and Colonial Drive (SR-50)). A total of sixty one subjects of age ranging from sixteen to sixty years were recruited to drive the simulator for the experiment, which consists of eight scenarios. This research validates the driving simulator for speed, safety and visual aspects. Based on the overall comparisons of speed between the simulated results and the real world, it was concluded that the UCF driving simulator is a valid tool for traffic studies related to driving speed behavior. Based on statistical analysis conducted on the experiment results, it is concluded that SR-434 northbound right turn lane and SR-50 eastbound through lanes have a higher rear-end crash risk than that at SR-50 westbound right turn lane and SR-434 northbound through lanes, respectively. This conforms to the risk of rear-end crashes observed at the actual intersection. Therefore, the simulator is validated for using it as an effective tool for traffic safety studies to test high-risk intersection locations. The driving simulator is also validated for physical and visual aspects of the intersection as 87.10% of the subjects recognized the intersection and were of the opinion that the replicated intersection was good enough or realistic. A binary logistic regression model was estimated and was used to quantify the relative rear-end crash risk at through lanes. It was found that in terms of rear-end crash risk SR50 east- bound approach is 23.67% riskier than the SR434 north-bound approach.