M.A. University of the Witwatersrand, Faculty of Humanities (Population Studies), 2012 / This study is based on secondary data analysis of the 1998 South African Demographic and Health Survey (SADHS) data set. The aim of the study was to identify socio-demographic, socio-economic and environmental variables that affect the survival of South African children under the age of five years. Descriptive analyses, frequency tables, Pearson’s chi-square tests of association and binary logistic regression analysis were used for data analysis in this study. Mothers who lost an under-five child were predominantly Black and rural. Such mothers were characterized by rural residential areas, relatively large family sizes, relatively poorer socioeconomic status, relatively poorer access to basic health services, relatively more child deliveries at home, and low level of education. The study showed that 269 of the 5, 066 children in the study died before celebrating their fifth birthday (5.31%). At the 5% level of significance, the survival of under-five children is significantly influenced by 2 of the 11 predictor variables found to be significantly associated in the univariate analysis and therefore included in the logistic regression analysis. These 2 predictor variables were: place of delivery of child [OR=0.97; P=0.000; CI = (0.96, 0.98)], and use of modern contraceptives by the mother [OR=0.73; P=0.002; CI = (0.59, 0.89)]. The study found that not using modern contraceptives gives a lower chance on death of a child under 5 as well as delivering at home, in the absence of a trained birth attendant. These findings are unexpected and contrary to what was found in the univariate analysis. No real explanation can be given for these findings and it would be interesting to see if the same results are found with more recent data. In order for the South African National Department of Health to fulfil its United Nations Millennium Development Goals, rural mothers and their under-five children must be provided with improved health as well as socioeconomic services.

In June 2016, the United Kingdom held its EU referendum, colloquially known as Brexit, in which the people of the island nation voted on whether their country should remain a member of or leave the European Union. This thesis investigates what economic variables may have lain behind the majority outcome of a given voting area (or district) and to what degree they may have impacted it. A logistic regression is conducted primarily on referendum and election data from the Electoral Commission, census data from the Office for National Statistics, and political leaning scores as quantified by the Manifesto Project. The resulting model, which exhibits a hit ratio of 92 percent correct predictions, shows that age, education, national identity, political leaning, irreligion, and unemployment have significant correlations with the majority Brexit outcome of a district. On the other hand, population, health, and income variables do not have statistically significant effects; however, poor health, on average, does seem to have a large positive effect on the odds when taking relative sample size into account.

The aim of the thesis is to investigate how the classification performance of random forest and logistic regression differ, given an imbalanced data set with MCAR missing data. The performance is measured in terms of accuracy and sensitivity. Two analyses are performed: one with a simulated data set and one application using data from the Swedish population registries. The simulation study is created to have the same class imbalance at 1:5. The missing values are handled using three different techniques: complete case analysis, predictive mean matching and mean imputation. The thesis concludes that logistic regression and random forest are on average equally accurate, with some instances of random forest outperforming logistic regression. Logistic regression consistently outperforms random forest with regards to sensitivity. This implies that logistic regression may be the best option for studies where the goal is to accurately predict outcomes in the minority class. None of the missing data techniques stood out in terms of performance.

Based on the curriculum of 2011 a new grading scale ranging from A-F was introduced in the Swedish upper secondary school system. Previous research on similar data have focused on the earlier grading scale, and its crucial that the new circumstances are addressed to understand the impact on grading. Using 348 essays from the national writing test this study investigates the use of automated essay scoring as a way of grading in this new setting. Using various classication methods the models for younger students outperform the corresponding models for older students. This implies that it is harder to predict grades on essays written by older students. Based on the current data the result shows that with the new grading scale the use of automated essay scoring should be used with caution.

Modelos de Credit Scoring são utilizados para estimar a probabilidade de um cliente proponente ao crédito se tornar inadimplente, em determinado período, baseadas em suas informações pessoais e financeiras. Neste trabalho, a técnica proposta em Credit Scoring é Redes Bayesianas (RB) e seus resultados foram comparados aos da Regressão Logística. As RB avaliadas foram as Bayesian Network Classifiers, conhecidas como Classificadores Bayesianos, com seguintes tipos de estrutura: Naive Bayes, Tree Augmented Naive Bayes (TAN) e General Bayesian Network (GBN). As estruturas das RB foram obtidas por Aprendizado de Estrutura a partir de uma base de dados real. Os desempenhos dos modelos foram avaliados e comparados através das taxas de acerto obtidas da Matriz de Confusão, da estatística Kolmogorov-Smirnov e coeficiente Gini. As amostras de desenvolvimento e de validação foram obtidas por Cross-Validation com 10 partições. A análise dos modelos ajustados mostrou que as RB e a Regressão Logística apresentaram desempenho similar, em relação a estatística Kolmogorov- Smirnov e ao coeficiente Gini. O Classificador TAN foi escolhido como o melhor modelo, pois apresentou o melhor desempenho nas previsões dos clientes maus pagadores e permitiu uma análise dos efeitos de interação entre variáveis. / Credit Scoring Models are used to estimate the insolvency probability of a customer, in a period, based on their personal and financial information. In this text, the proposed model for Credit Scoring is Bayesian Networks (BN) and its results were compared to Logistic Regression. The BN evaluated were the Bayesian Networks Classifiers, with structures of type: Naive Bayes, Tree Augmented Naive Bayes (TAN) and General Bayesian Network (GBN). The RB structures were developed using a Structure Learning technique from a real database. The models performance were evaluated and compared through the hit rates observed in Confusion Matrix, Kolmogorov-Smirnov statistic and Gini coefficient. The development and validation samples were obtained using a Cross-Validation criteria with 10-fold. The analysis showed that the fitted BN models have the same performance as the Logistic Regression Models, evaluating the Kolmogorov-Smirnov statistic and Gini coefficient. The TAN Classifier was selected as the best BN model, because it performed better in prediction of bad customers and allowed an interaction effects analysis between variables.

Os modelos de credit scoring têm sido bastante difundidos nos últimos anos como uma importante ferramenta para agilizar e tornar mais confiável o processo de concessão de crédito por parte das instituições financeiras. Esses modelos são utilizados para classificar os clientes em relação a seus riscos de inadimplência. Neste trabalho, é avaliada a aplicabilidade de uma nova metodologia, baseada em pseudo-valores, como alternativa para a construção de modelos de credit scoring. O objetivo é compará-la com abordagens tradicionais como a regressão logística e o modelo de riscos proporcionais de Cox. A aplicação prática é feita para dados de operações de crédito pessoal sem consignação, coletados do Sistema de Informações de Crédito do Banco Central do Brasil. As performances dos modelos são comparadas utilizando a estatística de Kolmogorov-Smirnov e a área sob a curva ROC. / Credit Scoring models have become popular in recent years as an important tool in the credit granting process, making it more expedite and reliable. The models are mainly considered to classify customers according to their default risk. In this work we evaluate the apllicability of a new methodology, based on pseudo-values, as an alternative to constructing credit scoring models. The objective is to compare this novel methodology with traditional approaches such as logistic regression and Cox proportional hazards model. The models are applied to a dataset on personal credit data, collected from the Credit Information System of Central Bank of Brazil. The performances of the models are compared via Kolmogorov-Smirnov statistic and the area under ROC curve.

Métodos analíticos para concessão de crédito vêm apresentando enormes avanços nas últimas décadas, particularmente no que se refere a métodos estatísticos de classificação para identificar grupos de indivíduos com diferentes taxas de inadimplência. A maioria dos trabalhos existentes sugere decisões do tipo conceder o crédito ou não, considerando apenas de forma marginal o resultado esperado da operação. O presente trabalho tem o objetivo de propor um modelo de avaliação de risco de crédito mais complexo que os tradicionais modelos de Credit Scoring, que forneça uma perspectiva mais detalhada acerca do desempenho futuro de um contrato de crédito, e que vá além da classificação entre bom e mau pagador. Aliado a este ganho de informação na previsibilidade oferecida pelo modelo, também é objetivo ampliar o espaço de decisões do problema, saindo de uma resposta binária (como aceitar/rejeitar o crédito) para algo que responda à seguinte pergunta: qual é a taxa justa para cobrir determinado risco?. / Analytical methods for granting credit are presenting enormous advances in recent decades, particularly in the field of statistical methods of classification to identify groups of individuals with different rates of default. Most of the existing work suggests decisions of the type granting credit or not, regarding just marginally the expected outcome of the operation. This work aims to propose a model to evaluate credit risk with more complexity than the traditional \"Credit Scoring\" models, providing a more detailed view about the future performance of a credit agreement, which goes beyond the classification of good and bad payers. Coupled with this improvement of information offered by the model, it is also this works aim to expand the decision space of the problem, leaving a binary response (such as accept/reject the claim) to something that answers the following question: \"what is the fair rate to cover a given risk \".

O efeito disposição, originalmente proposto por Shefrin e Statman (1985), preconiza que os investidores tendem a vender ações com lucro em um curto período de tempo e manter ações com prejuízo por um longo período de tempo. A despeito da ampla gama de evidências sobre o assunto, as razões que levariam os investidores a manifestar esse viés comportamental ainda é motivo de uma controvérsia importante entre motivações racionais e comportamentais. Neste trabalho, o objetivo foi testar duas motivações comportamentais concorrentes para explicar o efeito disposição: a teoria perspectiva e o viés da reversão à média. Para cumprir esse objetivo, foi feita uma análise das transações mensais de compra e venda de uma amostra de 51 fundos de investimento em ações brasileiros, no período de 2002 a 2008. A análise envolveu a estimação de dois modelos de regressão de variável dependente qualitativa. O primeiro consistiu em um modelo logit binário cujo propósito foi determinar a probabilidade de um gestor realizar um ganho ou uma perda de capital em razão de variáveis de retorno das ações. O segundo foi um modelo logit ordenado cujo objetivo foi verificar a existência de uma relação entre as variáveis de retorno e o volume monetário vendido das ações. Em ambos os modelos, os parâmetros estimados para as variáveis de retorno das ações foram interpretados como um coeficiente de disposição, sendo que a proposição desse coeficiente consistiu na principal contribuição da pesquisa. Os resultados dos modelos estimados trouxeram evidências de que a teoria perspectiva parece permear o processo decisório dos gestores dos fundos analisados. Já no caso da hipótese de que o efeito disposição é decorrente do viés da reversão à média, não foi possível corroborá-la com base nos resultados aqui relatados. / The disposition effect, originally proposed by Shefrin and Statman (1985), predicts that investors tend to sell winning stocks too soon and ride losing stocks too long. Despite the wide range of research evidence about this issue, the reasons that lead investors to act this way is still subject to much controversy between rational and behavioral explanations. In this thesis, the main goal was to test two competing behavioral motivations to justify the disposition effect: prospect theory and mean reversion bias. To achieve this goal, an analysis of monthly transactions for a sample of 51 Brazilian stock funds from 2002 to 2008 was conducted. The analysis involved the estimation of two regression models with qualitative dependent variable. The first one consisted of a binary logit model whose purpose was to set the probability of a manager to realize a capital gain or loss as a function of the stock return. The second one was an ordered logit model whose objective was to verify the existence of a relationship between stock returns and the monetary volume sold. In both models, the estimated parameters for the stock return variables were interpreted as a disposition coefficient and the proposition of this coefficient was the main contribution of the research. The results of the estimated models brought evidence that prospect theory seems to guide the decision making process of the managers of the analyzed funds. The hypothesis that the disposition effect is due to mean reversion bias could not be confirmed based on the results reported here.

Modelos de credit score são usualmente desenvolvidos somente com informações dos proponentes aceitos. Neste trabalho foram consideradas estratégias que podem ser utilizadas para o desenvolvimento de modelos de credit score com a inclusão das informações dos rejeitados. Foram avaliadas as seguintes técnicas de inferência de rejeitados: classificação dos rejeitados como clientes Maus, parcelamento, dados aumentados, uso de informações de mercado e ainda a estratégia de aceitar proponentes rejeitados para acompanhamento e desenvolvimento de novos modelos de risco de crédito. Para a avaliação e comparação dos modelos foram utilizadas as medidas de desempenho: estatística de Kolmogorov-Smirnov (KS), área sob a curva de Lorentz (ROC), área entre as curvas de distribuição acumulada dos escores (AEC), diferença entre as taxas de inadimplência nos intervalos do escore definidos pelos decis e coeficiente de Gini. Concluiu-se que dentre as quatro primeiras técnicas avaliadas, o uso de informaçõoes de mercado foi a que apresentou melhor desempenho. Quanto à estratégia de aceitar proponentes rejeitados, observou-se que há um ganho em relação ao modelo ajustado só com base nos proponentes aceitos. / Credit scoring models are usually built using only information of accepted applicants. This text considered strategies that can be used to develop credit score models with inclusion of the information of the rejects. We evaluated the techniques of reject inference: classification of rejected customers as bad, parceling, augmentation, use of market information and the strategy of accepting rejected proponents for monitoring and developing new models of credit risk. For the evaluation and comparison between models were used performance measures: Kolmogorov-Smirnov statistics (KS), the area under the Lorentz Curve (ROC), area between cumulative distribution curves of the scores (AEC), difference among the delinquency rate in the score buckets based on deciles (DTI) and the Gini coefficient. We concluded that among the first four techniques evaluated, the fourth (use of market information) had the best performance. For the strategy to accept rejected bidders, it was observed that there is a gain in relation to the model that uses only information of accepted applicants.

Contemporary computer vision solutions to the problem of object detection aim at incorporating contextual information into the process. This thesis proposes a systematic evaluation of the usefulness of incorporating knowledge about the geometric context of a scene into a baseline object detection algorithm based on local features. This research extends publicly available MATLABRª implementations of leading algorithms in the field and integrates them in a coherent and extensible way. Experiments are presented to compare the performance and accuracy between baseline and context-based detectors, using images from the recently published SUN09 dataset. Experimental results demonstrate that adding contextual information about the geometry of the scene improves the detector performance over the baseline case in 50% of the tested cases. / by Rafael J. Giusti Urbina. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.