Southern flounder (Parlichthys lethostigma) is a recreationally and commercially important flatfish species found in the Gulf of Mexico, and recent analyses indicate that the northern Gulf of Mexico population is in decline. For proper management, knowledge of habitats used throughout the juvenile stage is needed. The aim of the current study is to examine habitat use of young-of-year (YOY) southern flounder in the Galveston Bay complex using habitat distribution models and acoustic telemetry. A set of habitat distribution models examined how habitat use changes during the first year of life. In addition, southern flounder were tagged with acoustic telemetry transmitters and monitored with a novel receiver array that allows for measurements of fine-scale movements. These movements were compared to habitat maps to examine habitat selection. Habitat distribution models determined that habitat requirements for southern flounder change with ontogeny and season. Newly settled southern flounder were most influenced by physicochemical parameters and the presence of seagrass beds. YOY southern flounder, however, showed increased occurrence at freshwater inlets during summer and fall months, and occurrence decreased at tidal inlets during the fall. Predictions of habitat suitability across the Galveston Bay complex indicate that the factors influencing occurrence of southern flounder change with season, ontogeny, and availability of suitable habitats. With acoustic telemetry, it was apparent that habitat use by southern flounder was nonrandom and influenced by benthic and other physicochemical conditions. Habitat analyses indicated that southern flounder used sand habitats more frequently than seagrass, oyster reef, or salt marsh habitats. Telemetry results also indicated that depth and water temperature were important determinants of habitat suitability for YOY southern flounder, with individuals preferring deeper and cooler regions of the water column in Christmas Bay. Both model and telemetry analyses indicate that habitat use by YOY southern flounder is dynamic across multiple spatial and temporal scales, with distributions and movements influenced strongly by ontogenetic changes in habitat associations, temporal and spatial variability in physicochemical conditions, and tidal cycles.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-06-30T14:30:19Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) 3 - Dissertação - Jean Turet.pdf: 1592836 bytes, checksum: 61a949904ef7ff39905b9ae5637c906f (MD5) / Made available in DSpace on 2016-06-30T14:30:19Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) 3 - Dissertação - Jean Turet.pdf: 1592836 bytes, checksum: 61a949904ef7ff39905b9ae5637c906f (MD5) Previous issue date: 2015-12-10 / FACEPE / Dado o crescente aumento na utilização de smartphones e tablets, investimentos em m-commerce tornam-se uma boa oportunidade para o aumento nas vendas e, consequentemente, mais lucratividade para as empresas. Entretanto, para que haja sucesso neste investimento, torna-se necessário aumentar o grau de confiança dos usuários com o ambiente m-commerce das empresas que investem nesta modalidade de venda (também conhecidas como empresas em tempo real). As companhias têm se deparado com diversos desafios tecnológicos e organizacionais em m-commerce que podem, se não tratados corretamente, influenciar negativamente na confiança dos usuários e, consequentemente, afastar estes consumidores da utilização desta plataforma. Desta forma, a organização que deseja investir em m-commerce necessita minimizar ou sanar os impactos negativos que podem afastar o cliente do m-commerce. O uso adequado da Tecnologia da Informação e dos Sistemas de Informação são fundamentais para atender aos objetivos estratégicos da organização, bem como o sucesso neste ambiente de comércio virtual. Assim, para minimizar os problemas que os desafios do ambiente m-commerce impõem e aumentar esse grau de confiança dos usuários, as empresas deparam-se com a necessidade de executar melhorias no setor de Tecnologia da Informação. A impossibilidade de executar as ações estratégicas em paralelo impõe a necessidade de priorização de ações, que nem sempre é uma tarefa fácil. Nesse contexto, este trabalho procurou classificar esses desafios e apresentar um modelo multicritério baseado no modelo aditivo com veto para priorização das ações necessárias para melhorias neste ambiente de comércio virtual. Esse modelo procura minimizar alguns inconvenientes que podem acontecer devido ao efeito compensatório do modelo aditivo tradicional. / Due to the growth of the use of smartphones and tablets, investments in m-commerce seems to be a good opportunity to increase sales and, consequently companies’ profitability. However to achieve a successful investment is necessary increase the user confidence in m-commerce environment. In order to make it possible, enterprises are dealing with several technological and organizational challenges, which, if not well treated, can influence in a negative way, and move clients away. Therefore, any organization that wants to invest in m-commerce environment should solve those challenges or at least work to minimize the negative impacts that may exist. Moreover, the suitable use of information technology (IT) and information systems (IS) are essential to reach this goal and for sure the strategic objectives of the company. Aiming to increase the user confidence in m-commerce, companies are dealing with the necessity to improve their business plan and processes. For sure ranking a set of necessary actions is not an easy task. And so, this study tries to classify those challenges and proposes a multicriteria decision model based on the additive model with veto to ranking a set of actions. This model achieves the goal to minimize some inconvenient caused by the compensatory effect of the traditional additive model.

Orientadores: Roberto Wagner Lourenço, Maria Rita Donalísio Cordeiro / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-25T08:45:15Z (GMT). No. of bitstreams: 1 Barbosa_GersonLaurindo_D.pdf: 4382667 bytes, checksum: 11ab540bd4300b50d395f50692cbdec4 (MD5) Previous issue date: 2014 / Resumo: Diminuir os níveis de infestação pelo Aedes aegypti é uma das poucas estratégias para o controle da dengue na atualidade. O acompanhamento dos indicadores de infestação constitui parâmetro estratégico para as ações das equipes de controle da doença, porém pouco se sabe sobre a capacidade preditiva destes indicadores. Este trabalho tem como objetivo analisar a distribuição espacial dos indicadores entomológicos de Aedes aegypti nas fases ovos, larvas/pupas e mosquitos adultos e sua influência no risco de ocorrência de dengue em um município de médio porte no estado de São Paulo. Trata-se de um estudo caso-controle espacial, para avaliar a associação entre os indicadores entomológicos e o risco de dengue em Sumaré, SP, no ano de 2011. Os casos de dengue foram os confirmados e notificados pelo Sistema de Vigilância Epidemiológica da cidade e os controles foram obtidos por sorteio de pontos no perímetro da área habitada. Os indicadores entomológicos foram construídos a partir de coleta mensal de ovos (armadilhas), larvas/pupas e mosquitos adultos em quarteirões sorteados. Superfícies suavizadas dos valores dos indicadores entomológicos foram obtidas por meio do método de Krigagem ordinária. Estes indicadores foram incluídos no modelo aditivo generalizado para avaliar sua influência no risco espacial da doença. Observou-se ocorrência sazonal da doença e dos indicadores. Casos de dengue e vetores nas diversas fases do ciclo biológico foram encontrados em toda área de estudo. Entretanto, não houve coincidência espacial entre o risco da doença e a intensidade dos indicadores entomológicos. Os riscos relativos espaciais de dengue brutos e ajustados mostram feição espacial similar, indicando limitada interferência no risco da doença. Assim, a distribuição espacial e temporal da dengue possivelmente não depende da distribuição espacial dos vetores em locais onde os níveis de infestação são altos, antigos e estáveis, como no caso de Sumaré. Além disso, a área analisada apresenta infestação e transmissão antiga e deficiência de serviços públicos de saneamento e intensa circulação de pessoas, que podem ser fatores relevantes para explicar a circulação do vírus. O vetor foi identificado em abundância suficiente para desencadear e manter a circulação do vírus na área de estudo. A infestação não apresentou grande variação de intensidade e foi suficiente para a manutenção e/ou ocorrência de casos de dengue na área de estudo. O modelo aditivo generalizado não mostrou nenhum dos indicadores entomológicos analisados como preditores de áreas de risco de transmissão. A inclusão de outras variáveis nos modelos aditivos generalizados como sorotipos circulantes, imunidade populacional e intervenções por parte das equipes de controle poderiam eventualmente revelar efeito modulador do risco da doença, não encontrado utilizando-se apenas com os indicadores entomológicos / Abstract: Decrease the infestation levels of Aedes aegypti is one of the few strategies for dengue control today. Monitoring infestation indicators is strategic for the dengue control program, but little is known about the predictive capacity of these indicators. This study aimed to analyze the spatial distribution of entomological indicators of Aedes aegypti in the stages of egg, larva-pupae and adult forms and its influence on risk of dengue in a medium-sized city in the state of São Paulo. This is a spatial case-control study to evaluate the association between entomological indicators and risk of dengue in Sumaré, SP, in 2011. Dengue cases confirmed and reported by the Epidemiological Surveillance System of the municipality and the controls were obtained on the perimeter of the inhabited areas. Monthly entomological indicators were constructed from eggs, larvae-pupae and adult forms collected in the selected blocks. Smoothed surfaces for cases and entomological indicators were obtained by the ordinary kriging method. These indicators were included in the generalized additive model to assess its influence on the spatial risk of the disease. Seasonality of disease occurrence and entomological indicators were observed. Cases of dengue and vectors in the various life cycle stages were found throughout the study area. However, there was no spatial coincidence between disease risk and intensity of entomological indicators. The spatial crude and adjusted relative risks of dengue showed similar features, indicating its limited interference in disease risk. The spatial and temporal distribution of the disease may not depend exclusively on the spatial distribution of vectors in areas where infestation levels are high, longstanding and stable, like in the case of Sumaré-SP. Furthermore, the analyzed area has experienced dengue cases and high infestation for a long time and has poor public sanitation services and intense movement of persons, which may be relevant to explain the circulation of the virus. The vector was identified abundantly sufficient to initiate and maintain the virus in the study area. The infestation had no significant variation in intensity and was sufficient for the maintenance and / or occurrence of dengue cases in the study area. The entomological indicators analyzed in the generalized additive model didn¿t act as a predictor of the dengue risk in the area. Other variables as serotype circulation, the population immunity and interventions by the control teams could be included in the models in order to modulate disease risk, which was not found using only entomological indicators / Doutorado / Epidemiologia / Doutor em Saude Coletiva

Indiana University-Purdue University Indianapolis (IUPUI) / The essence of Bayesian data analysis is to ascertain posterior distributions. Posteriors generally do not have closed-form expressions for direct computation in practical applications. Analysts, therefore, resort to Markov Chain Monte Carlo (MCMC) methods for the generation of sample observations that approximate the desired posterior distribution. Standard MCMC methods simulate sample values from the desired posterior distribution via random proposals. As a result, the mechanism used to generate the proposals inevitably determines the efficiency of the algorithm. One of the modern MCMC techniques designed to explore the high-dimensional space more efficiently is Hamiltonian Monte Carlo (HMC), based on the Hamiltonian differential equations. Inspired by classical mechanics, these equations incorporate a latent variable to generate MCMC proposals that are likely to be accepted. This dissertation discusses how such a powerful computational approach can be used for implementing statistical models. Along this line, I created a unified computational procedure for using HMC to fit various types of statistical models. The procedure that I proposed can be applied to a broad class of models, including linear models, generalized linear models, mixed-effects models, and various types of semiparametric regression models. To facilitate the fitting of a diverse set of models, I incorporated new parameterization and decomposition schemes to ensure the numerical performance of Bayesian model fitting without sacrificing the procedure’s general applicability. As a concrete application, I demonstrate how to use the proposed procedure to fit a multivariate generalized additive model (GAM), a nonstandard statistical model with a complex covariance structure and numerous parameters. Byproducts of the research include two software packages that all practical data analysts to use the proposed computational method to fit their own models. The research’s main methodological contribution is the unified computational approach that it presents for Bayesian model fitting that can be used for standard and nonstandard statistical models. Availability of such a procedure has greatly enhanced statistical modelers’ toolbox for implementing new and nonstandard statistical models.

Nesta tese é analisado situações onde eventos de interesse podem ocorrer mais que uma vez para o mesmo indivíduo. Embora os estudos nessa área tenham recebido considerável atenção nos últimos anos, as técnicas que podem ser aplicadas a esses casos especiais ainda são pouco exploradas. Além disso, em problemas desse tipo, é razoável supor que existe dependência entre as observações. Uma das formas de incorporá-la é introduzir um efeito aleatório na modelagem da função de risco, dando origem aos modelos de fragilidade. Esses modelos, em análise de sobrevivência, visam descrever a heterogeneidade não observada entre as unidades em estudo. Os modelos estatísticos apresentados neste texto são fundamentalmente modelos de sobrevivência baseados em processos de contagem, onde é representado o problema como um processo de Poisson homogêneo e não-homogêneo com um termo de fragilidade, para o qual um indivíduo com um dado vetor de covariável x é acometido pela ocorrência de eventos repetidos. Esses modelos estão divididos em duas classes: modelos de fragilidade multiplicativos e aditivos; ambos visam responder às diferentes formas de avaliar a influência da heterogeneidade entre as unidades na função de intensidade dos processos de contagem. Até agora, a maioria dos estudos tem usado a distribuição gama para o termo de fragilidade, a qual é matematicamente conveniente. Este trabalho mostra que a distribuição gaussiana inversa tem propriedade igualmente simples à distribuição gama. Consequências das diferentes distribuições são examinadas, visando mostrar que a escolha da distribuição de fragilidade é importante. O objetivo deste trabalho é propor alguns métodos estatísticos para a análise de eventos recorrentes e verificar o efeito da introdução do termo aleatório no modelo por meio do estudo do custo, da estimação dos outros parâmetros de interesse. Também um estudo de simulação bootstrap é apresentado para fazer inferências dos parâmetros de interesse. Além disso, uma abordagem Bayesiana é proposta para os modelos de fragilidade multiplicativos e aditivos. Métodos de simulações são utilizados para avaliar as quantidades de interesse a posteriori. Por fim para ilustrar a metodologia, considera-se um conjunto de dados reais sobre um estudo dos resultados experimentais de animais cancerígenos. / In this thesis we analyse situations where events of interest may occur more than once for the same individual and it is reasonable to assume that there is dependency among the observations. A way of incorporating this dependency is to introduce a random effect in the modelling include a frailty term in the intensity function. The statistical methods presented here are intensity models based, where we represent the problem as a homogeneous and nonhomogeneous Poisson process with a frailty term for which an individual with given fixed covariate vector x has reccurent events occuring. These models are divided into two classes: multiplicative and additive models, aiming to answer the different ways of assessing the influence of heterogeneity among individuals in the intensity function of the couting processes. Until now most of the studies have used a frailty gamma distribution, due to mathematical convenience. In this work however we show that a frailty gaussian inverse distribution has equally simple proprieties when compared to a frailty gamma distribution. Methods for regression analysis are presented where we verify the effect of the frailty term in the model through of the study of the cost of estimating the other parameters of interest. We also use the simulation bootstrap method to make inference on the parameters of interest. Besides we develop a Bayesian approach for the homogeneous and nonhomogeneous Poisson process with multiplicative and additive frailty. Simulation methods are used to assess the posterior quantities of interest. In order to ilustrate our methodology we considere a real data set on results of an experimental animal carcinogenesis study.

The seagrass resource provides essential ecosystem functions for many marine species. This research evaluated the vulnerability and sustainability of the seagrass resource in an urbanized area to the effects of sea level rise. The assessment required analysis of information regarding the biogeography of the seagrass resource, and developing a method to model the spatial extent of the suitable habitat for seagrass, and applying the model to predict the implications of simulated sea level rise scenarios on the seagrass resource. Examining the biogeography of the seagrass resource required the development of a seagrass monitoring and assessment field survey and a comprehensive seagrass resource map (SGRM). The mesoscale field survey was designed and conducted in St. Joseph Sound (STJS) and Clearwater Harbor North (CLWN), Pinellas County, Florida from 2006-2010 to determine the seagrass species composition and spatial distribution for the resource. The seagrass species found in the study area consisted of Syringodium filiforme Kützing (Syringodium), Thalassia testudinum Banks ex König (Thalassia), and Halodule wrightii Ascherson (Halodule). These seagrass species occurred in monospecific and mixed beds in all combinations throughout the study area. Spatially, Thalassia was the dominant nearshore in STJS and Halodule in CLWN. Syringodium was most frequently found in STJS in the mid to deep depths. The SGRM was mapped from satellite remote sensing imagery with training information from the mesoscale field survey data. Landsat 5 Thematic Mapper (TM) and Earth Observing-1 Hyperion (HYP) were processed to map the seagrass resource in the study area in a nearshore shallow coastal area of Pinellas County, FL, USA. A maximum likelihood classification (MLC) was used to classify both TM and HYP imagery into three classes (seagrass estimated coverage) of the seagrass resource. The overall accuracy for the TM MLC map was 91% (kappa = 0.85) and the HYP was 95% (kappa = 0.92). Due to areas of cloud cover in the HYP image, it was necessary to composite the classification values from the TM MLC to accurately define these areas. The validation accuracy (n=72) of the composite seagrass resource map was 81% which was much more rigorous than the previous accuracy estimates. These results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource. The development of a spatial habitat suitability model (HSM) for the seagrass resource provided a management tool to better understand the relationship between seagrass, water quality, and other environmental factors. The motivation to develop the spatial HSM was to provide a spatial modeling tool to simulate changes in the water quality environment and evaluate the potential impact on the seagrass resource. High resolution bathymetry and field survey water quality data were used to fit general additive models (GAM) to the STJS (Adjusted R2= 0.72, n=134) and CLWN (Adjusted R2= 0.75, n=138) seagrass resource. The final GAMs included water quality variables including salinity, chlorophyll-a concentration, total suspended solids, turbidity, and light. The only significant variable was the light metric in STJS (p-value= 0.001) and CLWN (p-value= 0.006). The light metric was the logarithmic light attenuation calculated from the water quality field survey transmittance (660nm) data and the high resolution bathymetry. The overall accuracy (OA) of the predictive GAM rasters was higher in CLWN (95%, kappa =0.88) than in STJS (82%, kappa = 0.40). The increased prediction error in STJS was spatially correlated with the areas of lower density seagrass along the deep edge of the bed. While there may be a plethora of factors contributing to the decreased density of the seagrass, this may indicate these seagrass were already living at the edge of the suitable habitat. Factors threatening the sustainability of the seagrass resource included those related to water quality and environmental changes. Knowledge of these relationships was essential to develop a predictive spatial HSM to simulate responses of the seagrass to changes in the water quality and the environment. Historically, environmental management strategies focused on water quality targets, but have not considered mitigation for climate change impacts, specifically sea level rise (SLR). This study utilized the HSM for the seagrass resource as a management tool to better understand the relationship between seagrass, water quality, and sea level rise scenarios. Based on SLR scenarios for 1ft-6ft (0.305m-1.83m) from 2010 to 2100, the potential seagrass habitat loss and gain was analyzed. From the current 60 km2 of seagrass habitat in St. Joseph Sound (STJS) and Clearwater Harbor North (CLWN), the predicted seagrass habitat loss based on the HSM which focused on light availability for photosynthesis ranged from 14 km2 (SLR 1ft) to 26 km2 (SLR 2ft) to the entire 60 km2 (SLR 6ft). The potential seagrass habitat gain based on the coastal flooding model (NOAA, 2012) ranged from 4 km2 (SLR 1ft) to 19 km2 (SLR 6ft). However, based on the spatial distribution of the seagrass and the proximity of the seagrass to the new habitat, the potential viable habitat based on the mean seagrass growth rates (horizontal rhizome elongation) only ranged from 2 km2 (SLR 1ft) to 9 km2 (SLR 6ft). An additional complexity to the gain of seagrass habitat was the effect of the anthropogenically altered shorelines, seawalls, which covered 47% of the shoreline. These seawalls potentially could impede the inundation of the seawater and the seagrass colonization of these areas by creating a vertical boundary for seagrass growth. The mitigation of the potential effects of SLR on the seagrass resource may require ecosystem level management. While management of water quality would continue to benefit the seagrass resource, additional management strategies would be necessary to mitigate for potential decrease in suitable seagrass habitat related to the effects of SLR. A discussion of potential management approaches suggested that the integration of coastal shoreline management strategies and seagrass resource management would be essential to insure the sustainability of the resource.

No description available.

Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-21T12:34:57Z No. of bitstreams: 1 DissLDS.pdf: 1642291 bytes, checksum: 745a8cbfe2eeb392253fc4364b0d4c3b (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-23T18:20:44Z (GMT) No. of bitstreams: 1 DissLDS.pdf: 1642291 bytes, checksum: 745a8cbfe2eeb392253fc4364b0d4c3b (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-23T18:20:47Z (GMT) No. of bitstreams: 1 DissLDS.pdf: 1642291 bytes, checksum: 745a8cbfe2eeb392253fc4364b0d4c3b (MD5) / Made available in DSpace on 2016-09-23T18:20:51Z (GMT). No. of bitstreams: 1 DissLDS.pdf: 1642291 bytes, checksum: 745a8cbfe2eeb392253fc4364b0d4c3b (MD5) Previous issue date: 2015-05-08 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Ecological restoration is an emerging field of restoration ecology, and is widely used as an important tool for biodiversity conservation. However, appropriate indicative allocations of soil quality for the assessment and monitoring of forest restoration projects are still scarce. The objective of this study was to evaluate soil quality of four restored forests of different ages, a forest remnant and their agricultural surroundings in the State of São Paulo through chemical, physical and microbiological soil parameters by categorization by the weighted additive model. To measure the discrimination of soils due to the different locations and types of land use, the weighted additive model, along with a soil quality index (SQI) and principal component analysis (PCA) were used. The PCA differentiated the forests of their agricultural environments and locations with a prevalence of physical and chemical parameters in the distinction between the matrices. The parameters soil organic matter, total cation exchange capacity, phosphorus, soil bulk density, porosity, microbial biomass carbon and microbial activity had higher averages in the forests matrices, being higher in older forests. There was differentiation in soil quality of the studied forests, where the highest rates were obtained in the remaining forest soils and older restored forests. All forest areas showed higher rates compared to their respective agricultural environments. Finally, the SQI used were efficient for the identification of the areas, having potential as a tool to support decision-making on environmental issues. / A restauração ecológica é uma área emergente da ecologia da restauração, sendo amplamente empregada como uma importante ferramenta para a conservação da biodiversidade. Contudo, subsídios indicativos da qualidade do solo apropriados para a avaliação e monitoramento de projetos de restauração florestal ainda são escassos. O objetivo deste trabalho foi avaliar a qualidade do solo de quatro florestas restauradas de diferentes idades, um remanescente florestal e seus respectivos entornos agrícolas no Estado de São Paulo através de seus atributos químicos, físicos e microbiológicos por categorização pelo modelo aditivo ponderado. Para discriminação dos solos em função das diferentes localidades e tipos de cobertura foram utilizados o MAP, juntamente com um índice de qualidade do solo (IQS) e análises de componentes principais. As análises de componentes principais diferenciaram as florestas de seus entornos agrícolas e localidades, havendo predomínio de parâmetros físicos e químicos na contribuição para a distinção entre as matrizes. Os parâmetros matéria orgânica do solo, capacidade de troca catiônica total, teor de fósforo, densidade do solo, porosidade total, carbono da biomassa microbiana e atividade microbiana apresentaram médias superiores sob as matrizes florestais, sendo maiores nas florestas mais antigas. Houve diferenciação na qualidade do solo das florestas estudadas, onde os maiores valores de IQS foram obtidos nos solos do remanescente florestal e nos projetos de restauração florestal mais antigos. Todas as áreas florestais apresentaram maiores valores de IQS em comparação a seus respectivos entornos agrícolas. Conclui-se que o modelo utilizado para o cálculo do IQS foi eficiente na diferenciação das áreas, tendo potencial como ferramenta no auxílio da tomada de decisões na temática ambiental.

Nesta tese é analisado situações onde eventos de interesse podem ocorrer mais que uma vez para o mesmo indivíduo. Embora os estudos nessa área tenham recebido considerável atenção nos últimos anos, as técnicas que podem ser aplicadas a esses casos especiais ainda são pouco exploradas. Além disso, em problemas desse tipo, é razoável supor que existe dependência entre as observações. Uma das formas de incorporá-la é introduzir um efeito aleatório na modelagem da função de risco, dando origem aos modelos de fragilidade. Esses modelos, em análise de sobrevivência, visam descrever a heterogeneidade não observada entre as unidades em estudo. Os modelos estatísticos apresentados neste texto são fundamentalmente modelos de sobrevivência baseados em processos de contagem, onde é representado o problema como um processo de Poisson homogêneo e não-homogêneo com um termo de fragilidade, para o qual um indivíduo com um dado vetor de covariável x é acometido pela ocorrência de eventos repetidos. Esses modelos estão divididos em duas classes: modelos de fragilidade multiplicativos e aditivos; ambos visam responder às diferentes formas de avaliar a influência da heterogeneidade entre as unidades na função de intensidade dos processos de contagem. Até agora, a maioria dos estudos tem usado a distribuição gama para o termo de fragilidade, a qual é matematicamente conveniente. Este trabalho mostra que a distribuição gaussiana inversa tem propriedade igualmente simples à distribuição gama. Consequências das diferentes distribuições são examinadas, visando mostrar que a escolha da distribuição de fragilidade é importante. O objetivo deste trabalho é propor alguns métodos estatísticos para a análise de eventos recorrentes e verificar o efeito da introdução do termo aleatório no modelo por meio do estudo do custo, da estimação dos outros parâmetros de interesse. Também um estudo de simulação bootstrap é apresentado para fazer inferências dos parâmetros de interesse. Além disso, uma abordagem Bayesiana é proposta para os modelos de fragilidade multiplicativos e aditivos. Métodos de simulações são utilizados para avaliar as quantidades de interesse a posteriori. Por fim para ilustrar a metodologia, considera-se um conjunto de dados reais sobre um estudo dos resultados experimentais de animais cancerígenos. / In this thesis we analyse situations where events of interest may occur more than once for the same individual and it is reasonable to assume that there is dependency among the observations. A way of incorporating this dependency is to introduce a random effect in the modelling include a frailty term in the intensity function. The statistical methods presented here are intensity models based, where we represent the problem as a homogeneous and nonhomogeneous Poisson process with a frailty term for which an individual with given fixed covariate vector x has reccurent events occuring. These models are divided into two classes: multiplicative and additive models, aiming to answer the different ways of assessing the influence of heterogeneity among individuals in the intensity function of the couting processes. Until now most of the studies have used a frailty gamma distribution, due to mathematical convenience. In this work however we show that a frailty gaussian inverse distribution has equally simple proprieties when compared to a frailty gamma distribution. Methods for regression analysis are presented where we verify the effect of the frailty term in the model through of the study of the cost of estimating the other parameters of interest. We also use the simulation bootstrap method to make inference on the parameters of interest. Besides we develop a Bayesian approach for the homogeneous and nonhomogeneous Poisson process with multiplicative and additive frailty. Simulation methods are used to assess the posterior quantities of interest. In order to ilustrate our methodology we considere a real data set on results of an experimental animal carcinogenesis study.

No description available.