Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-05-23T16:23:56Z No. of bitstreams: 1 DissAMCF.pdf: 1964890 bytes, checksum: 84bcbd06f74840be6fc5f38659c34c07 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-06-05T19:07:22Z (GMT) No. of bitstreams: 1 DissAMCF.pdf: 1964890 bytes, checksum: 84bcbd06f74840be6fc5f38659c34c07 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-06-05T19:07:28Z (GMT) No. of bitstreams: 1 DissAMCF.pdf: 1964890 bytes, checksum: 84bcbd06f74840be6fc5f38659c34c07 (MD5) / Made available in DSpace on 2017-06-05T19:18:45Z (GMT). No. of bitstreams: 1 DissAMCF.pdf: 1964890 bytes, checksum: 84bcbd06f74840be6fc5f38659c34c07 (MD5) Previous issue date: 2016-12-16 / Não recebi financiamento / The objective of this work is to study the binary regression model under the frequentist and Bayesian approaches using the probit, logit, log-log complement, Box-Cox transformation and skewprobit as link functions. In the classical approach we presented assumpti- ons and procedures used in the regression modeling. We verified the accuracy of the estimated parameters by building confidence intervals and conducting hypothesis tests. In the Bayesian appro- ach we made a comparative study using two methodologies. For the first methodology, we considered non-informative prior dis- tributions and the Metropolis-Hastings algorithm to estimate the model. In the second methodology we used auxiliary variables to obtain the known a posteriori distribution, allowing the use of the Gibbs Sampler algorithm. However, the introduction of these auxiliary variables can generate correlated values and needs the use of clustering of unknown quantities in blocks to reduce the autocorrelation. In the simulation study we used the AIC and BIC information criteria to select the most appropriate model and we evaluated whether the coverage probabilities of the confidence interval is in agre- ement with that expected by the asymptotic theory. In Bayesian approach we found that the inclusion of auxiliary variables in the model results in a more efficient algoritm according to the MSE, MAPE and SMAPE criteria. In this work we also present applications to two real datasets. The first dataset used is the variation of the Ibovespa and variation of the daily value of the American dollar at the time of closing the 2013 to 2016. The second dataset, used is an educational data set (INEP-2013), where we are interested in studying the factors that infuence the approval of the student. / Este trabalho tem como objetivo estudar o modelo de regressão binária nas abordagens clássica e bayesiana utilizando as funcoes de ligacoes probito, logito, complemento log-log, transformaçao box-cox e probito-assimetrico. Na abordagem clássica apresentamos as suposicoes e o procedimento para ajustar o modelo de regressao e verificamos a precisão dos parâmetros estimados, construindo intervalos de confianca e testes de hipóteses. Enquanto que, na inferência bayesiana fizemos um estudo comparativo utilizando duas metodologias. Na primeira metodologia consideramos densidades a priori nao informativas e utilizamos o algoritmo Metropolis-Hastings para ajustar o modelo. Na segunda metodologia utilizamos variáaveis auxiliares para obter a distribuiçcaão a posteriori conhecida, facilitando a implementacão do algoritmo do Amostrador de Gibbs. No entanto, a introduçao destas variaveis auxiliares podem gerar valores correlacionados, o que leva à necessidade de se utilizar o agrupamento das quantidades desconhecidas em blocos para reduzir a autocorrelaçcãao. Atraves do estudo de simulacao mostramos que na inferência classica podemos usar os critérios AIC e BIC para escolher o melhor modelo e avaliamos se o percentual de cobertura do intervalo de confianca assintotica está de acordo com o esperado na teoria assintática. Na inferência bayesiana constatamos que o uso de va-riaáveis auxiliares resulta em um algoritmo mais eficiente segundo os critérios: erro quadrâtico medio (EQM), erro percentual absoluto medio (MAPE) e erro percentual absoluto medio simetrico (SMAPE). Como ilustração apresentamos duas aplicações com dados reais. Na primeira, consideramos um conjunto de dados da variaçao do Ibovespa e a variacao do valor diário do fechamento da cotacao do dólar no período de 2013 a 2016. Na segunda aplicação, trabalhamos com um conjunto de dados educacionais (INEP-2013), focando nos estudos das variaveis que influenciam a aprovacao do aluno.

Orientador: Maria Márcia Pereira Sartori / Resumo: O estudo da longevidade é uma ferramenta importante na análise da qualidade fisiológica em sementes. A modelagem da curva de sobrevivência em sementes permite a predição do seu período de vida, que baliza os mais variados estudos em conservação e tecnologia de sementes. O modelo de Probit foi inicialmente proposto como o modelo ideal para predição da longevidade das sementes, contudo, estudos têm reportado certa dificuldade de predição do modelo em diferentes condições de estresse e armazenagem a que as sementes são submetidas. A equação da viabilidade em sementes a partir do modelo de Probit permite calcular o valor do P50, que é o período em que um lote de sementes leva para perder 50% da sua viabilidade. O modelo de Logit é similar ao de Probit, com a vantagem de ser mais simples, e de se adequar melhor ao comportamento dos dados com caudas pesadas. Assim, o objetivo deste estudo foi avaliar os modelos de Probit e Logit quanto a sua robustez na predição da longevidade das sementes. Para tanto, sementes de soja foram selecionadas quanto ao seu vigor, em delineamento inteiramente casualizado, e armazenadas à 35°C e 75% de umidade relativa, até que fosse constatada sua morte, por meio de testes de germinação realizados periodicamente. A construção das curvas de sobrevivência, após o experimento encerrado, permitiu a análise dos modelos de Probit e Logit, por meio dos parâmetros R2, Rajustado, e do coeficiente de correlação de Pearson. O estudo da normalidade dos resíduos tamb... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The study of longevity is an important tool in the analysis of physiological quality in seeds. The modeling of the survival curve in seeds allows the prediction of their half time life, which could be used to reference for the most varied studies on conservation and seed technology. The Probit model was initially proposed as the ideal model for seed longevity prediction, however, studies have reported about some errors found after applying the model under different stress and storage conditions in which seeds are submitted. The seed viability equation from the Probit model allows to calculate the value of P50, which is the period in which a seed lot loss 50% of its viability. The Logit model is similar to the Probit model, with the advantage of being simpler and better suited to heavy tails data, as occurs in seed longevity data. The aim of this study was to evaluate the Probit and Logit models for their robustness in predicting seed longevity. For this purpose, soybean seeds were selected according to their vigor, in a completely randomized design, and stored in 35 °C and 75% relative humidity until their death was verified by periodic germination tests. The construction of survival curves, after the experiment ended, allowed the analysis of Probit and Logit models, through the parameters R2 , Radjusted, and the Pearson correlation coefficient. The study of the normality of the residues was also performed to evaluate the models. The results showed the superiority of the Logi... (Complete abstract click electronic access below) / Doutor

MSc (Statistics) / Department of Statistics / This study explores the use of exible link functions in discrete survival models through a simulation study and an application to the Swaziland Demographic and Health Survey (SDHS) data. The objective of the research study is to perform simulation exercises in order to compare the e ectiveness of di erent families of link functions and to construct a discrete multilevel survival model for age at rst marriage among women in Swaziland using a exible link function. The Pareto hazard model, Pregibon and Gosset families of link functions were considered in models with and without unobserved heterogeneity. The Pareto model where the family parameter is estimated from the data was found to outperform the other models, followed by the Pregibon and the Gosset family of link functions. The results from both simulation study and real data analysis of the SDHS data illustrated that, misspecication of the link function causes bias on the estimation of results. This demonstrates the importance of choosing the right link. The ndings of this study reveal that women who are highly educated, stay in the Manzini and Shiselweni region, those who reside in urban areas were more likely to marry later compared to their counterparts in Swaziland. The results also reveal that the proportion of early rst marriages is declining since the di erence among birth cohorts is found to be very high, with women of younger cohorts getting married later compared to older women. / NRF

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / Over the past several years DoD imposed constraints on test deliverables, requiring objective measures of test results, i.e., statistically defensible test and evaluation (SDT&E) methods and results. These constraints force testers to employ statistical hypotheses, analyses and modeling to assess test results objectively, i.e., based on statistical metrics, analytical methods, probability of confidence complemented by, rather than solely on expertise, which is too subjective. In this and companion papers we discuss methods of objectifying testing. We employ an earth coordinate model and statistical modeling of telemetry (TM) tracking antenna employing time-space position information (TSPI) and derived statistical measures for tracking-error and auto-tracking mode. Test data were statistically analyzed via analysis of covariance (ANCOVA) which revealed that the antenna control unit (ACU) under test (AUT) does not track statistically identically, nor as practically or efficiently in C-band while receiving data carriers in both S- and C-bands. The conclusions of this paper add support to that hypothesis. In this third of three papers we use data from a range test, but make no reference to the systems under test as the purpose of this paper is to present an example of tools useful for employing a SDT&E methodology.

La survie nette est un indicateur très utilisé en épidémiologie des cancers. Il s'agit de la survie que l'on observerait si la seule cause de mortalité était le cancer ; il est le seul indicateur épidémiologique utilisable à des fins de comparaisons de survie (entre périodes/pays) car il s'affranchit des éventuelles différences de mortalité dues aux autres causes que le cancer. Le premier objectif de notre travail était d'analyser les performances des différentes méthodes d'estimation de la survie nette sur données simulées ainsi que sur données réelles afin que les méthodes non biaisées soient reconnues scientifiquement et soient les seules à être utilisées par la suite. Nous avons ainsi démontré que deux approches étaient capables d'estimer sans biais la survie nette : l'approche non paramétrique de Pohar-Perme et l'approche reposant sur une modélisation multivariée du taux de mortalité en excès dû au cancer. Cette dernière approche impose une stratégie de construction difficile à mettre en place. Le deuxième objectif était de développer une boîte à outils composée de différents tests permettant de vérifier les différentes hypothèses faites lors de la construction d'un modèle de régression du taux de mortalité en excès. Ces hypothèses concernent habituellement la proportionnalité ou non de l'effet des covariables, leur forme fonctionnelle, ainsi que la fonction de lien utilisée. Le troisième objectif était une application épidémiologique qui visait à étudier l'impact des facteurs pronostiques, tel que le stade au diagnostic, sur la survie nette conditionnelle, en d'autres termes sur la dynamique du taux de mortalité en excès, après la survenue d'un cancer du côlon / Net survival is one of the most important indicators in cancer epidemiology. It is defined as the survival that would be observed if cancer were the only cause of death. This is the only one indicator allowing comparisons of cancer impact between countries or time periods because it is not influenced by death because of other causes. The first objective of this work was to compare the performance of several estimators of the net survival in a simulation study and then on real data in order to promote unbiased methods. Those methods are the non-parametric Pohar-Perme method and the parametric multivariable excess rate model. The latest one needs a model building strategy. The use of diagnostic procedures for model checking is an essential part of the modeling process. The second objective was to develop a tool box composed of diagnostic tools allowing to check hypothesis usually considered when constructing an excess mortality rate model, that is, the proportionality or not of the effect of covariates, their functional form and the link function. The third objective deals with the study of the impact of prognostic variables, such as stage at diagnosis, on conditional net survival, that is, on the dynamic of the excess hazard mortality after the diagnosis of colon cancer