A number of articles only present hurdle models for count data. we are motivated to present hurdle models for semi-continuous data. Because semi- continuous data is also commonly seen in non-life insurance. The thesis deals with the parameterization of various hurdle models for semi-continuous data besides for count data in non-life insurance. Two components of a hurdle model are modeled separately. A hurdle component is modeled by a logistic regression. For a semi-continuous data, a continuous component is modeled by several various regressions. Parameters of each component are estimated through maximum likelihood estimation. Model selection is mentioned before theoretical approaches are applied on the vehicle insurance data. Finally, we get some predicted values based on the fitted models. The prediction gives insurance companies a general idea on setting premium but not accurate. 1

A presente dissertação tem como objetivo propor uma sistemática para a construção de modelos de previsão de risco de crédito e também comparar o desempenho de três técnicas estatísticas multivariadas utilizadas para sua construção: análise discriminante, regressão logística e redes neurais. O método proposto (denominado Modelo PRC) é composto de seis etapas: (i) delimitação da população; (ii) seleção da amostra; (iii) análise preliminar; (iv) construção do modelo; (v) escolha do modelo e (vi) passos para implantação. O Modelo PRC foi aplicado em uma amostra de 17.005 clientes de uma rede de farmácias com crediário próprio. Os resultados encontrados demonstram uma superioridade das redes neurais em relação às outras duas técnicas, o que era esperado devido a sua abordagem nãolinear na combinação das variáveis. Considerando a venda anual aos clientes da base em estudo e utilizando o modelo neural desenvolvido, estima-se um acréscimo de 65% nos lucros. / This work presents a methodology for credit risk prediction, comparing the performance of three statistical techniques used in the prediction process: discriminant analysis, logistic regression and neural networks. The proposed method (entitled PRC Model) embraces six steps: (i) population definition, (ii) sampling, (iii) preliminary analysis, (iv) model development, (v) model selection and (vi) implementation steps. The PRC Model was applied to a sample of 17,005 customers from an organization, which manages his own credit system and controls a pool of drugstores. The results show the superiority of neural networks over the other two techniques. This was expected due to the non-linear approach of the neural network when dealing with the explanatory variables. Considering the neural network model and the annual sales due to customers included on this study, the use of the proposed methodology indicates a 65% potential profits.

Estudos com o objetivo de prever insolvência de empresas e que fazem uso de técnicas estatísticas modernas são conduzidos desde a década de 1960. Esta linha de pesquisa, que inicialmente usou técnicas univariadas, e em seguida incorporou as análises multivariadas, hoje emprega largamente técnicas que fazem uso de inteligência artificial e que necessitam uma grande capacidade de processamento computacional. Esta evolução trouxe melhorias contínuas aos resultados alcançados e hoje é possível afirmar que os demonstrativos financeiros de empresas quando analisados adequadamente são uma fonte importante de informação para a previsão de insolvência. Esta pesquisa teve como principal objetivo desenvolver e comparar modelos estatísticos usando as técnicas de Análise Discriminante Linear, Regressão Logística e Redes Neurais Artificiais a fim de investigar qual delas oferece os melhores resultados. A amostra foi composta por 40 empresas, divididas em dois grupos: o primeiro com empresas formalmente insolventes segundo os critérios da legislação brasileira, e o segundo com empresas sem tais problemas. Foram usadas inicialmente 16 variáveis para predição e empregou-se um critério de seleção de variáveis baseado nos melhores subconjuntos possíveis ao invés do stepwise. Foi tomado especial cuidado com os pré-requisitos das técnicas, sobretudo da Análise Discriminante, como normalidade e ausência de multicolinearidade das variáveis independentes. Os resultados das previsões obtidas com os modelos foram coerentes com o esperado, ou seja, a Análise Discriminante teve um desempenho inferior à Regressão Logística que também foi superada pelas Redes Neurais Artificiais. / Researches in bankruptcy prediction of companies that make use of modern statistics techniques are being held since the 1960’s. This branch of study, which initially employed univariate techniques, and then assimilated the multivariate techniques today uses artificial intelligence, a techniques that needs a great computational processing capability. This evolution brought continuing improvements to the results achieved and today is possible to say that financial statements when properly analyzed are a good source of information to the prediction of financial distress. This research aimed mainly the development of prediction models using Discriminant Analysis, Logistic Regression and Artificial Neural Networks so that they could be compared in terms of predictive capabilities. The sample consisted of 40 firms divided in 2 groups (bankrupt and non bankrupt companies) according to the Brazilian bankruptcy law. The 16 initial predictors were selected to enter the model according to the best subsets procedure in order than the stepwise procedure. Special attention was taken to accomplish the pre-requisites of the techniques, above all the Discriminant Analysis, like normality and lack of multicollinearity of the independent variables. The findings of the predictions were reasonable and according to what was expected: the Discriminant Analysis was outperformed by the Logistic Regression that was also outperformed by the Artificial Neural Networks.

Esta pesquisa investiga um possível conflito de abordagens de eficiência de 16 bancos corporate (segmento empresarial) brasileiros entre 1996 a 2015 através da análise envoltória de dados (DEA) em dois estágios. No primeiro estágio, é utilizada a ferramenta DEA para estudar a eficiência sob as três principais abordagens de avaliação: intermediação, produção e rentabilidade para cada instituição financeira e período. O segundo estágio é composto pela regressão logística, cujo objetivo é identificar indicadores financeiros do modelo CAMEL - acrônimo que representa cinco estruturas de avaliação de risco bancário: adequação de capital, qualidade dos ativos, qualidade da gestão, rentabilidade, liquidez - e receita operacional (indicador de porte) que estejam relacionados com a eficiência. Os resultados indicam evidências da existência de conflito entre as abordagens, revelando dois perfis opostos de bancos: 1) eficientes em intermediação e produção, cujo grupo é formado por bancos de grande porte, que atuam em segmentos de empresas corporate, com menores índices de imobilização, spread, rentabilidade do ativo, custo de captação e maiores índices de alavancagem, rentabilidade bancária, liquidez e operações de crédito e, 2) eficientes em rentabilidade, cujo grupo é formado pelos bancos de pequeno porte, com foco em segmento small e middle market e indicadores inversos ao do primeiro grupo. Além disso, foi identificado que há indícios de que a eficiência também está relacionada a fatores externos como taxa básica de juros e taxa de câmbio. Estes resultados contribuem com informações relevantes para órgãos reguladores, investidores e gestores de bancos / This study investigate a possible conflict of efficiency approaches of 16 Brazilian corporate banks from 1996 to 2015 through two-stage data envelopment analysis (DEA). In the first stage, DEA is used to study the efficiency at three main approaches: intermediation, production and profitability for each financial institution and period. The second stage consists in a logistic regression, which the objective is to identify the financial indicators of the CAMEL model - an acronym that represents five structures of bank risks\' evaluation: capital, assests, management, equity and liquidity - and operating income (size indicator) that are related to efficiency. The results show that there is evidence of conflict between approaches, revealing two opposite bank profiles: 1) efficient in intermediation and production, which is formed by big banks, focused on corporate companies, with low indicators of immobilization, spread, return on assets, cost of raising funds, and high indicators of bank profitability, leverage, liquidity and credit operations and 2) efficient in profitability, which is formed by small banks, focused on small and middle market segment and with opposite indicators compared to the first group. Besides that, it was identified that is possible that the efficiency is related to external factors like basic interest tax and exchange rate. This results contribute with relevant information to regulators, investors and bank managers

A inovação tecnológica desempenha um papel fundamental no desenvolvimento de empresas, regiões e mesmo de países. Especificamente no estado de São Paulo, estudar os aspectos relevantes a este tema é de suma importância por se tratar do estado mais industrializado e mais importante economicamente no Brasil. Dentro deste contexto, este estudo visa analisar especificamente aspectos ligados à inovação tecnológica nas empresas dos diversos setores de atividade industrial, utilizando para isto ndicadores de inovação tecnológica e de dados empresariais da Pesquisa de Atividade Econômica Paulista (PAEP), realizada pela fundação Sistema Estadual de Análise de Dados Estatísticos (SEADE), sobre o período de 1999 a 2001. / The technological innovation performs a fundamental part in the development process of companies, regions and even countries. Specifically in the state of São Paulo, the study of relevant aspects to this theme is of summary importance because it is the most industrialized and economically important in this country. Within of this context, this study aim to analyze specifically some aspects linked to the technological innovation in different sections of industrial activity, using to this, technological innovation indicators and business results obtained by the Paulista Research of Economic Activities (PAEP), that was realized by SEADE foundation over the period of 1999 to 2001.

Orientador: Olívio Novaski / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-23T02:56:25Z (GMT). No. of bitstreams: 1 CooperOrdonez_RobertEduardo_D.pdf: 3017595 bytes, checksum: cd030a2dd41c13c03a265b2befebeb50 (MD5) Previous issue date: 2013 / Resumo: O presente trabalho tem por objetivo propor um modelo para usar os conceitos da Corrente Crítica no gerenciamento de sistemas de múltiplos projetos. Para tal, foi desenvolvida uma pesquisa aplicada, de natureza qualitativa, em ambiente real. As variáveis estudadas foram definidas considerando a revisão da literatura científica e um estudo de campo realizado com antecipação à aplicação do modelo, o qual busca, por meio de uma visão sistêmica, gerenciar melhor a incerteza presente na estimativa de tempo das atividades dos projetos. As diretrizes da pesquisa-ação foram usadas para verificar o funcionamento do modelo e para o levantamento dos dados que posteriormente foram analisados por meio da técnica estatística Regressão Logística Binária. Essa técnica possibilitou encontrar o nível de impacto das variáveis de influência sobre a resposta do sistema, assim como as relações entre essas variáveis. Os dados analisados permitem sugerir que o modelo proposto funciona adequadamente e que os resultados obtidos poderiam ser trasladados do contexto estudado para outros contextos, contribuindo desta forma para o aprimoramento do método da Corrente Crítica / Abstract: The present work aims to propose a model for using the Critical Chain concepts for multiple projects management. To this, it developed an applied research in a real environment. Variables were defined considering scientific literature review and a field study carried out in advance of the application model, which seeks, through a systemic view, a better manage of the uncertainty present in the estimated time of project activities. The guidelines of action-research were used to check the performance of the model and the data collection were later analyzed by statistical technique: Binary Logistic Regression. This technique makes possible to find the impact level of influence variables and the relation with de system response, as well the relationships between them. The analyzed data may suggested that the proposed model works properly and that results could be transferred from de studied context to other contexts, thus contributing to the improvement of the Critical Chain method / Doutorado / Materiais e Processos de Fabricação / Doutor em Engenharia Mecânica

Datainsamling har genomförts i Tarfaladalen under 5 dagar vid månadsskiftet mellan mars och april 2017. Insamlingen resulterade i 36 BTS-mätningar (Bottom Temperature of Snow cover) som därefter har använts tillsammans med data från tidigare insamlingar, till att skapa en sammanställd modell över förekomsten av permafrost omkring Tarfala. En statistisk undersökning syftade till att identifiera meningsfulla parametrar som permafrost beror av, genom att testa de oberoende variablerna mot BTS i en stegvis regression. De oberoende faktorerna höjd över havet, aspekt, solinstrålning, vinkel och gradient hos sluttningar producerades för varje undersökt BTS-punkt i ett geografiskt informationssystem.                 Den stegvisa regressionen valde enbart höjden som signifikant variabel, höjden användes i en logistisk regression för att modellera permafrostens utbredning. Den slutliga modellen visade att permafrostens sannolikhet ökar med höjden. För att skilja mellan kontinuerlig, diskontinuerlig och sporadisk permafrost delades modellen in i tre zoner med olika sannolikhetsspann. Den kontinuerliga permafrosten är högst belägen och därav den zon där sannolikheten för permafrost är störst, denna zon gränsar till den diskontinuerliga permafrosten vid en höjd på 1523 m. Den diskontinuerliga permafrosten har en sannolikhet mellan 50–80 % och dess undre gräns på 1108 m.ö.h. separerar den diskontinuerliga zonen från den sporadiska permafrosten / A field data collection has been carried out in Tarfala valley at the turn of March to April 2017. The collection resulted in 36 BTS-measurements (Bottom Temperature of Snow cover) that has been used in combination with data from earlier surveys, to create a model of the occurrence of permafrost around Tarfala. To identify meaningful parameters that permafrost relies on, independent variables were tested against BTS in a stepwise regression. The independent variables elevation, aspect, solar radiation, slope angle and curvature were produced for each investigated BTS-point in a geographic information system.                 The stepwise regression selected elevation as the only significant variable, elevation was applied to a logistic regression to model the permafrost occurrence. The final model showed that the probability of permafrost increases with height. To distinguish between continuous, discontinuous and sporadic permafrost, the model was divided into three zones with intervals of probability. The continuous permafrost is the highest located zone and therefore has the highest likelihood, this zone delimits the discontinuous permafrost at 1523 m a.s.l. The discontinuous permafrost has probabilities between 50-80 % and its lower limit at 1108 m a.s.l. separates the discontinuous zone from the sporadic permafrost.

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The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases

Le travail soumis commence par un aperçu du micro-crédit tel qu’il a été introduit au Bangladesh par M. Yunus. Puis on donne un modèle stochastique des retards de versement. Comme ces retards ne donnent pas lieu à une sanction financière, ils constituent, de fait, une baisse du taux réel de crédit. Ce taux est alors, lui-même, aléatoire. On calcule un taux espéré en fonction de la probabilité de retard de remboursement hebdomadaire. On déduit que ce taux espéré est d’environ 3.5% inférieur au taux (annoncé) du cas déterministe si l’on considère que 3% des retards atteignent 4 semaines. Le travail se poursuit par une étude statistique de données du micro-crédit en Thaïlande. On commence par présenter un modèle de régression logistique du taux de remboursement par rapport aux 23 variables mesurées sur un échantillon de 219 groupes d’emprunteurs. On présente ensuite une sélection des variables les plus pertinentes selon un critère AIC ou BIC par une méthode “backward stepwise”. Finalement des expériences sur des sous-échantillons montrent une bonne stabilité du choix des variables obtenues par la sélection. / This study is inspired from a real scenario of microcredit lending introduced in Bangladesh by Yunus. A stochastic model of random delays in repayment installments is then constructed. Since delays occur without financial penalty, the interest rate is obviously lower than the exact claimed. This rate then becomes a random variable corresponding to the random repayment time, in which simulation results of its distribution are provided. The expected rate is computed as a function of in-time installment probability. It is found around 3.5% lower than the exact one in the deterministic case when considering 3% of delay occurred within four weeks in real practice. The work is extended to a statistical analysis on data of microcredit in Thailand. It is started by presenting a logistic regression model of repayment outcome containing 23 input variables measured on a sample of 219 lending groups. Applying penalized criterion, AIC or BIC together with backward stepwise elimination procedure on the full model, a more parsimonious model kept only most relevant predictors is obtained. Finally, experiments on sub-samples show a stability of the chosen predictors obtained by the selection method.