Today's rapid changes and major business developments in organizations increase the need for effective management teams. In management teams, there are significant demands on the members to understand how strategic, tactical and operational decisions and actions generate results. High management team effectiveness requires optimum cooperation between the members with particular emphasis on well-operated communication and ability and flexibility in working as a team. It further requires a deep understanding of the factors that influence the management team effectiveness. The challenge to create a theoretical dynamic model to indicate development potentials regarding the effectiveness in the management teams represents the foundation for the idea behind this master thesis. This master thesis presents a theoretical management dynamic model I have developed based on identified key factors that influence the effectiveness of management teams. For identification of these key factors, I have used literary studies and research concerning the concept of team, management team, team effectiveness, leading organizations, organization development, dynamic models and many other concepts. I have categorized these key factors in five criteria. These criteria are engagement and dynamic leadership, team spirit, management meetings, conflict management and visions and objectives. In view of that, my definition of an effective management team is: team where high-engaged and motivated members including a strategic and dynamic leader work in a team having a good team spirit, hold effective management meetings and manage conflicts effectively to make qualified decisions that mainly are concentrated to reach welldefined bjectives and visions”.  The inspired idea behind my model is to integrate these criteria in the mechanical system called the Planetary Gear System to create a metaphoric image describing the dynamic of management teams and their effectiveness. Strategies for measuring these criteria also are identified and presented in this master thesis. These properties make the present dynamic model to a unique model in its appearance and functionality. The main function of my model is to indicate development potentials in the management teams. These development potentials are then used to give the studied management team relevant recommendations aimed at making the management team more effective. The aim of this master thesis is to investigate whether the developed model fulfill this function. In order to investigate the ability of the model to fulfill this function the model has been applied to a real management team. The results have shown that the model has sufficient ability to indicate development potentials in the studied management team. The obtained results have been analyzed using SPSS computer program. Based on these results several recommendations are given. In this manner, the model has fulfilled stated expectations. However, a couple of additional actions aimed at increasing the qualifications of the presented dynamic model are identified at the end of this master thesis. With the intention of verifying whether the developed model contributes to make the studied management team more effective, the performed measurement should be repeated after a period of at least six months. The re-measurement is necessary to follow up the effect of the given recommendations and also to indicate any new development potential. Such a task is recommended for further research and development of the model.

<p>Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient methods for Bayesian inference for simultaneous estimation of latent states and unknown fixed parameters.</p><p>Chapter 1 introduces state space models and methods of inference in these models. Chapter 2 describes a novel method for jointly sampling the entire latent state vector in a nonlinear Gaussian state space model using a computationally efficient adaptive mixture modeling procedure. This method is embedded in an overall Markov chain Monte Carlo algorithm for estimating fixed parameters as well as states. In Chapter 3 the method of the previous chapter is implemented in a few illustrative</p><p>nonlinear models and compared to standard existing methods. This chapter also looks at the effect of the number of mixture components as well as length of the time series on the efficiency of the method. I then turn to an biological application in Chapter 4. I discuss modeling choices as well as derivation of the state space model to be used in this application. Parameter and state estimation are analyzed in these models for both simulated and real data. Chapter 5 extends the methodology introduced in Chapter 2 from nonlinear Gaussian models to general state space models. The method is then applied to a financial</p><p>stochastic volatility model on US $ - British £ exchange rates. Bayesian inference in the previous chapter is accomplished through Markov chain Monte Carlo which is suitable for batch analyses, but computationally limiting in sequential analysis. Chapter 6 introduces sequential Monte Carlo. It discusses two methods currently available for simultaneous sequential estimation of latent states and fixed parameters and then introduces a novel algorithm that reduces the key, limiting degeneracy issue while being usable in a wide model class. Chapter 7 implements the novel algorithm in a disease surveillance context modeling influenza epidemics. Finally, Chapter 8 suggests areas for future work in both modeling and Bayesian inference. Several appendices provide detailed technical support material as well as relevant related work.</p> / Dissertation

Uncertainties in both loading and structural characteristics can adversely affect the response and reliability of a structure. Parameter uncertainties in structural dynamics can arise due to several sources. These include variations due to intrinsic material property variability, measurement errors, manufacturing and assembly errors, differences in modeling and solution procedures. Problems of structural dynamics with randomly distributed spatial inhomogeneities in elastic, mass, and damping properties, have been receiving wide attention. Several mathematical and computational issues include discretization of random fields, characterization of random eigensolutions, inversion of random matrices, solutions of stochastic boundary-value problems, and description of random matrix products. Difficulties are encountered when one has to include interaction between nonlinear and stochastic system characteristics, or if one is interested in controlling the system response. The study of structural systems including the effects of system nonlinearity in the presence of parameter uncertainties presents serious challenges and difficulties to designers and reliability engineers. In the analysis of large structures, the situation for substructuring frequently arises due to the repetition of identical assemblages (substructures), within a structure, and the general need to reduce the size of the problem, particularly in the case of non-linear inelastic dynamic analysis. A small reduction in the model size can have a large effect on the storage and time requirement. A primary structural dynamic system may be coupled to subsystems such as piping systems in a nuclear reactor or in a chemical plant. Usually subsystem in itself is quite complex and its modeling with finite elements may result in a large number of degrees of freedom. The reduced subsystem model should be of low-order yet capturing the essential dynamics of the subsystem for useful integration with the primary structure. There are two major issues to be studied: one, techniques for analyzing a complex structure into component subsystems, analyzing the individual sub-system dynamics, and from thereon determining the dynamics of the structure after assembling the subsystems. The nonlinearity due to support gap effects such as supports for piping system in nuclear reactors further complicates the problem. The second is the issue of reviewing the methods for reducing the model-order of the component subsystems such that the order of the global dynamics, after assembly, is within some predefined limits. In the reliability analysis of complex engineering structures, a very large number of the system parameters have to be considered as random variables. The parameter uncertainties are modeled as random variables and are assumed to be time independent. Here the problem would be to reduce the number of random variables without sacrificing the accuracy of the reliability analysis. The procedure involves the reduction of the size of the vector of random variables before the calculation of failure probability. The objectives of this thesis are: 1.To use the available model reduction techniques in order to effectively reduce the size of the finite element model, and hence, compare the dynamic responses from such models. 2.Study of propagation of uncertainties in the reduced order/coupled stochastic finite element dynamic models. 3.Addressing the localized nonlinearities due to support gap effects in the built up structures, and also in cases of sudden change in soil behaviour under the footings. The irregularity in soil behaviour due to lateral escape of soil due to failure of quay walls/retaining walls/excavation in neighbouring site, etc. 4.To evolve a procedure for the reduction of size of the vector containing the random variables before the calculation of failure probability. In the reliability analysis of complex engineering structures, a very large number of the system parameters are considered to be random variables. Here the problem would be to reduce the number of random variables without sacrificing the accuracy of the reliability analysis. 5.To analyze the reduced nonlinear stochastic dynamic system (with phase space reduction), and effectively using the network pruning technique for the solution, and also to use filter theory (wavelet theory) for reducing the input earthquake record to save computational time and cost. It is believed that the techniques described provide highly useful insights into the manner structural uncertainties propagate. The cross-sectional area, length, modulus of elasticity and mass density of the structural components are assumed as random variables. Since both the random and design variables are expressed in a discretized parameter space, the stochastic sensitivity function can be modeled in a parallel way. The response of the structures in frequency domain is considered. This thesis is organized into seven chapters. This thesis deals with the reduced order models of the stochastic structural systems under deterministic/random loads. The Chapter 1 consists of a brief introduction to the field of study. In Chapter 2, an extensive literature survey based on the previous works on model order reduction and the response variability of the structural dynamic systems is presented. The discussion on parameter uncertainties, stochastic finite element method, and reliability analysis of structures is covered. The importance of reducing mechanical models for dynamic response variability, the systems with high-dimensional variables and reduction in random variables space, nonlinearity issues are discussed. The next few chapters from Chapter 3 to Chapter 6 are the main contributions in this thesis, on model reduction under various situations for both linear and nonlinear systems. After forming a framework for model reduction, local nonlinearities like support gaps in structural elements are considered. Next, the effect of reduction in number of random variables is tackled. Finally influence of network pruning and decomposition of input signals into low and high frequency parts are investigated. The details are as under. In Chapter 3, the issue of finite element model reduction is looked into. The generalized finite element analysis of the full model of a randomly parametered structure is carried out under a harmonic input. Different well accepted finite element model reduction techniques are used for FE model reduction in the stochastic dynamic system. The structural parameters like, mass density and modulus of elasticity of the structural elements are considered to be non-Gaussian random variables. Since the variables considered here are strictly positive, the probabilistic distribution of the random variables is assumed to be lognormal. The sensitivities in the eigen solutions are compared. The response statistics based on response of models in frequency domain are compared. The dynamic responses of the full FE model, separated into real and imaginary parts, are statistically compared with those from reduced FE models. Monte Carlo simulation is done to validate the analysis results from SFEM. In Chapter 4, the problem of coupling of substructures in a large and complex structure, and FE model reduction, e.g., component mode synthesis (CMS) is studied in the stochastic environment. Here again, the statistics of the response from full model and reduced models are compared. The issues of non-proportional damping, support gap effects and/local nonlinearity are considered in the stochastic sense. Monte Carlo simulation is done to validate the analysis results from SFEM. In Chapter 5, the reduction in size of the vector of random variables in the reliability analysis is attempted. Here, the relative entropy/ K-L divergence/mutual information, between the random variables is considered as a measure for ranking of random variables to study the influence of each random variable on the response/reliability of the structure. The probabilistic distribution of the random variables is considered to be lognormal. The reliability analysis is carried out with the well known Bucher and Bourgund algorithm (1990), along with the probabilistic model reduction of the stochastic structural dynamic systems, within the framework of response surface method. The reduction in number of random variables reduces the computational effort required to construct an approximate closed form expression in response surface approach. In Chapter 6, issues regarding the nonlinearity effects in the reduced stochastic structural dynamic systems (with phase space reduction), along with network pruning are attempted. The network pruning is also adopted for reduction in computational effort. The earthquake accelerogram is decomposed using Fast Mallat Algorithm (Wavelet theory) into smaller number of points and the dynamic analysis of structures is carried out against these reduced points, effectively reducing the computational time and cost. Chapter 7 outlines the contributions made in this thesis, together with a few suggestions made for further research. All the finite element codes were developed using MATLAB5.3. Final pages of the thesis contain the references made in the preparation of this thesis.

Submitted by Leticia Klotz Silva (leticia.klotz@yahoo.com.br) on 2014-05-22T20:03:36Z No. of bitstreams: 1 Dissertacao.pdf: 954000 bytes, checksum: e6f9ad03459719a76f932ca5a316ec2b (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2014-05-26T15:09:46Z (GMT) No. of bitstreams: 1 Dissertacao.pdf: 954000 bytes, checksum: e6f9ad03459719a76f932ca5a316ec2b (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2014-05-28T17:18:53Z (GMT) No. of bitstreams: 1 Dissertacao.pdf: 954000 bytes, checksum: e6f9ad03459719a76f932ca5a316ec2b (MD5) / Made available in DSpace on 2014-05-28T17:19:12Z (GMT). No. of bitstreams: 1 Dissertacao.pdf: 954000 bytes, checksum: e6f9ad03459719a76f932ca5a316ec2b (MD5) Previous issue date: 2014-04-30 / Este trabalho investiga como os padrões de compras de consumidores de bens estocáveis são afetados por suas expectativas de preços. Usando um modelo dinâmico padrão de maximização da utilidade, deriva-se uma expressão analítica para as compras dos consumidores como uma função das suas expectativas em relação aos preços futuros. Em seguida, uma versão mais tratável do modelo é construída, de forma a ilustrar graficamente como os diferentes tipos de expectativas de preços implicam diferentes padrões de compras dos consumidores. Além disso, na aplicação empírica, investigo qual o modelo de expectativas de preços, entre aqueles comumente utilizados na literatura, é consistente com os dados. Por fim, encontra-se suficiente heterogeneidade em expectativa de preços dos consumidores. Mostra-se que famílias de pequeno porte acreditam que os preços seguem um processo de Markov de primeira ordem, enquanto famílias de alta renda são racionais. / I investigate how patterns of consumers’ purchases of storable goods are affected by their price expectations. Using a standard dynamic model of utility maximization, an analytical expression of consumer’s purchase is derived as a function of consumers expectations regarding future prices. Then, I construct a more tractable version of that model to see graphically how different types of price expectations imply different patterns of purchases for the consumers. Moreover, in the empirical application, I investigate which model of price expectations, among those commonly used in the literature, is consistent with the data. Finally, I find strong heterogeneity in con- sumers price expectations, showing that small size households believe prices follow a first order Markov process, and high income households are rational.

A análise de dados de sobrevivência tem sido tradicionalmente baseada no modelo de regressão de Cox (COX, 1972). No entanto, a suposição de taxas de falha proporcionais assumida para esse modelo pode não ser atendida em diversas situações práticas. Essa restrição do modelo de Cox tem gerado interesse em abordagens alternativas, dentre elas os modelos dinâmicos que permitem efeito das covariáveis variando no tempo. Neste trabalho, foram revisados os principais modelos de sobrevivência dinâmicos com estrutura aditiva e multiplicativa nos contextos não paramétrico e semiparamétrico. Métodos gráficos baseados em resíduos foram apresentados com a finalidade de avaliar a qualidade de ajuste desses modelos. Uma versão tempo-dependente da área sob a curva ROC, denotada por AUC(t), foi proposta com a finalidade de avaliar e comparar a qualidade de predição entre modelos de sobrevivência com estruturas aditiva e multiplicativa. O desempenho da AUC(t) foi avaliado por meio de um estudo de simulação. Dados de três estudos descritos na literatura foram também analisados para ilustrar ou complementar os cenários que foram considerados no estudo de simulação. De modo geral, os resultados obtidos indicaram que os métodos gráficos apresentados para avaliar a adequação dos modelos em conjunto com a AUC(t) se constituem em um conjunto de ferramentas estatísticas úteis para o próposito de avaliar modelos de sobrevivência dinâmicos nos contextos não paramétrico e semiparamétrico. Além disso, a aplicação desse conjunto de ferramentas em alguns conjuntos de dados evidenciou que se, por um lado, os modelos dinâmicos são atrativos por permitirem covariáveis tempo-dependentes, por outro lado podem não ser apropriados para todos os conjuntos de dados, tendo em vista que estimação pode apresentar restrições para alguns deles. / Analysis of survival data has been traditionally based on the Cox regression model (COX, 1972). However, the proportionality of the hazards required by this model may not be attended for many practical situations. This restriction of the Cox model has generated interest in alternative approaches, among them dynamic models that allow covariates with time-varying effect. In this work, the main dynamic survival models with additive and multiplicative structures were revised under the nonparametric and semiparametric settings. Graphical methods based on residuals were presented in order to evaluate the goodness-of-fit of these models. A time-dependent version of the area under the ROC curve, denoted by AUC(t), was proposed to evaluate and compare the predictive accuracy of additive and multiplicative survival models. The performance of the AUC(t) was evaluated by means of a simulation study. Data from three studies described in the literature were also analyzed to illustrate or complement the scenarios that were considered in the simulation study. Overall, the results indicate that the graphical methods presented to assess the goodness-of-fit of the models together with the AUC(t) provide a useful set of statistics tools for the purpose of evaluating dynamic survival models in the nonparametric and semiparametric settings. Moreover, applying this set of tools in some data sets showed that on the one hand dynamic models are attractive because they allow time-dependent covariates, but on the other hand they may not be appropriate for all data sets since estimation may present restrictions for some of them.

O objetivo principal do presente trabalho é oferecer métodos alternativos de previsão da arrecadação tributária federal, baseados em metodologias de séries temporais, inclusive com a utilização de variáveis explicativas, que reflitam a influência do cenário macroeconômico na arrecadação tributária, com o intuito de melhorar a acurácia da previsão da arrecadação. Para tanto, foram aplicadas as metodologias de modelos dinâmicos univariados, multivariados, quais sejam, Função de Transferência, Auto-regressão Vetorial (VAR), VAR com correção de erro (VEC), Equações Simultâneas, e de modelos Estruturais. O trabalho tem abrangência regional e limita-se à análise de três séries mensais da arrecadação, relativas ao Imposto de Importação, Imposto Sobre a Renda das Pessoas Jurídicas e Contribuição para o Financiamento da Seguridade Social - Cofins, no âmbito da jurisdição do estado de São Paulo, no período de 2000 a 2007. Os resultados das previsões dos modelos acima citados são comparados entre si, com a modelagem ARIMA e com o método dos indicadores, atualmente utilizado pela Secretaria da Receita Federal do Brasil (RFB) para previsão anual da arrecadação tributária, por meio da raiz do erro médio quadrático de previsão (RMSE). A redução média do RMSE foi de 42% em relação ao erro cometido pelo método dos indicadores e de 35% em relação à modelagem ARIMA, além da drástica redução do erro anual de previsão. A utilização de metodologias de séries temporais para a previsão da arrecadação de receitas federais mostrou ser uma alternativa viável ao método dos indicadores, contribuindo para previsões mais precisas, tornando-se ferramenta segura de apoio para a tomada de decisões dos gestores. / The main objective of this work is to offer alternative methods for federal tax revenue forecasting, based on methodologies of time series, inclusively with the use of explanatory variables, which reflect the influence of the macroeconomic scenario in the tax collection, for the purpose of improving the accuracy of revenues forecasting. Therefore, there were applied the methodologies of univariate dynamic models, multivariate, namely, Transfer Function, Vector Autoregression (VAR), VAR with error correction (VEC), Simultaneous Equations, and Structural Models. The work has a regional scope and it is limited to the analysis of three series of monthly tax collection of the Import Duty, the Income Tax Law over Legal Entities Revenue and the Contribution for the Social Security Financing Cofins, under the jurisdiction of the state of São Paulo in the period from 2000 to 2007. The results of the forecasts from the models above were compared with each other, with the ARIMA moulding and with the indicators method, currently used by the Secretaria da Receita Federal do Brasil (RFB) to annual foresee of the tax collection, through the root mean square error of approximation (RMSE). The average reduction of RMSE was 42% compared to the error committed by the method of indicators and 35% of the ARIMA model, besides the drastic reduction in the annual forecast error. The use of time-series methodologies to forecast the collection of federal revenues has proved to be a viable alternative to the method of indicators, contributing for more accurate predictions, becoming a safe support tool for the managers decision making process.

Streptococcus pneumoniae est une bactérie cocci gram positif commensale de la flore oropharyngée qui colonise le rhinopharynx de l’Homme et dont près de 100 sérotypes sont connus. Les nourrissons et les jeunes enfants représentent son réservoir principal. Le pneumocoque peut être à l’origine d’infections graves, telles que la méningite, les bactériémies et la pneumonie, et moins graves mais plus courantes comme la sinusite et l’otite moyenne aiguë. Deux vaccins anti-pneumococciques conjugués ont été introduits en France : le PCV7 (couvrant contre 7 sérotypes) en 2003 et le PCV13 (couvrant contre 6 sérotypes supplémentaires) en 2010. L’objectif général de ce travail de thèse est d’évaluer l’impact des politiques vaccinales sur les infections invasives à pneumocoque en France, en s’intéressant principalement aux évolutions temporelles et géographiques des plus graves : les méningites à pneumocoque (MP). Un premier travail a étudié les dynamiques temporelles des MP sur la période 2001–2014 afin d’identifier l’impact de l’introduction des vaccins conjugués. Des techniques statistiques de modélisations adaptées aux séries temporelles ont été utilisées. Les résultats de ce travail retrouvent des effets rapportés dans la littérature : une réduction des MP à sérotypes vaccinaux mais aussi une augmentation des MP dues aux sérotypes non inclus dans le vaccin (phénomène de « remplacement sérotypique »).Par conséquent, le premier bénéfice, à l’échelle de la population générale, de l’introduction de cette vaccination a été observé seulement onze ans après l’introduction du PCV7, et principalement suite à l’introduction du PCV13 en 2010, avec une diminution de 25% du nombre de MP en 2014. La composante géographique a ensuite été prise en compte afin d’étudier le rôle de la de couverture vaccinale dans la variabilité des MP annuelles entre les départements sur la période 2001-2016. Les résultats confirment l’efficacité des deux formulations du vaccin sur les MP dues aux sérotypes vaccinaux et suggèrent une certaine homogénéité de cet effet entre les différents départements. Inversement, le remplacement sérotypique a été confirmé mais uniquement suite à l’introduction de la première formulation du vaccin et ces effets présentent une répartition géographique hétérogène et variable. La variabilité de la couverture vaccinale entre les départements n’explique pas celle observée dans le nombre de MP, ce qui suggère l’intervention d’autres facteurs tel que la densité géographique. Enfin, une modélisation dynamique, permettant de prendre en compte des aspects fondamentaux des dynamiques de transmission et d’infection du pneumocoque non intégrés dans les méthodes de modélisation statique, a été proposée afin de prédire l’impact de différentes stratégies de vaccination pour les adultes de 65 ans et plus et ainsi évaluer leur rapport coût-utilité. / Streptococcus pneumoniae is a Gram-positive commensal bacterium of the oropharyngeal flora usually colonizing human’s rhino pharynx, of which almost 100 serotypes are known. Infants and young children constitute its main reservoir. Pneumococcus may cause serious infections, such as meningitis, bacteremia and pneumonia, or less serious but more common such as sinusitis and acute otitis media (AOM). Two conjugate pneumococcal vaccines have been introduced in France: PCV7 (covering 7 serotypes) in 2003 and PCV13 (covering 6 additional serotypes) in 2010. The overall objective of this thesis is to assess the impact of vaccination policy on invasive pneumococcal diseases in France, by focusing on temporal and geographical trends of the most serious of them: pneumococcal meningitis (PM). An initial study of PMs temporal dynamics over the 2011-2014 period assessed the impact of conjugate vaccines’ introduction. Statistical modeling techniques were used for time series analysis. The results confirm the effects found in literature: a reduction of vaccine serotypes PMs but at the same time an increase of PMs, due to non-vaccine serotypes (effect of “serotype replacement”). Therefore, the first benefit of vaccine introduction at population scale has been observed no less than 11 years after PCV7 introduction, and then principally after PCV13 was introduced in 2010, with a 25% decrease in PMs in 2014. The geographic component was then implemented to analyze the role of vaccine coverage in annual PM variability between geographic units over the 2001-2016 period. Results confirm the effectiveness of both vaccine compositions on vaccine serotypes PMs and suggest homogeneity of this effect among geographic units. Conversely the serotype replacement has been confirmed only after the first vaccine composition was introduced and presents a variable and heterogeneous geographical repartition. Variability in vaccine coverage among geographic units doesn’t explain the differences in PMs, which could suggest the role of others factors such as demographic density. Finally, a dynamic modeling capable of taking into consideration fundamental aspects of pneumococcus transmission and infection mechanisms not integrated in static modeling has been proposed in order to predict the impacts of different vaccination strategies for 65+ adults and therefore assess their cost-utility ratios.

This cumulative dissertation extends the literature strand on dynamic trade-off models in corporate finance. While Kane et al. (1984) and Fischer et al. (1989) have been probably first in developing dynamic trade-off models incorporating the effects of debt financing, it was Leland (1994) that really started the contingent claims revolution in corporate finance (Strebulaev and Whited, 2011, p. 25). Over the last 25 years, a whole strand of literature extended Leland's basic model to shed light on various financial decisions. To just provide some examples: Goldstein, Ju, and Leland (2001) based their model on a stochastic EBIT-process and allowed for an option to increase debt in order to understand the dynamic adjustment of capital structures. Strebulaev (2007) included external shocks into his capital structure models and gave indications on how capital structure tests shall be conducted. The contributions achieved in the essays of this dissertation focus on (i) more realistic conditions of default, (ii) an improved understanding of observed debt maturities and (iii) capital structures, (iv) the risk of applying the Equity IRR in financial decision making, as well as (v) the optimal choice between project financing and corporate financing.

Este trabalho apresenta um estudo de dados com resposta em intervalos limitados, mais especificamente no intervalo [0,1], como no caso de taxas e proporções. Em diversos casos práticos esta estrutura de dados apresenta uma quantidade não negligenciável de valores extremos (0 e 1) e que modelos usuais não são adequados para sua análise. Para esta situação propomos, por meio de um enfoque Bayesiano, modelos de regressão beta inflacionado de zeros e uns (BIZU) e modelos de regressão Tobit duplamente censurado adaptados nesse intervalo. Técnicas de diagnóstico e qualidade do ajuste também são discutidas. Apresentamos a análise desta estrutura de dados no contexto de série de tempo por meio da abordagem Bayesiana de modelos dinâmicos. Estudos de comportamento e previsão de séries de tempo foram explorados utilizando técnicas de Monte Carlo sequencial, conhecidas como filtro de partículas. Particularidades e competitividade entre as duas classes de modelos também foram discutidas. / This paper presents a study focused on observations in a limited interval , more specifically in [0,1] , such as rate and proportion data. In many practical cases this data structure has a considerable amount of extreme values (0 and 1) and usual classical models are not suitable for this type of data set. We propose two class of regression models to deal with this context: beta inflated of zeros and ones (BIZU) models and Tobit doubly censored models adapted in this interval. Fit quality and diagnostic techniques are also discussed. Time series of proportions are also developed through Bayesian dynamic models. Forecasting and behavioral analysis were explored using sequential Monte Carlo techniques, known as particle filters. Particularities and competitiveness between the two classes of models were also discussed as well.

Este trabalho apresenta um estudo de dados com resposta em intervalos limitados, mais especificamente no intervalo [0,1], como no caso de taxas e proporções. Em diversos casos práticos esta estrutura de dados apresenta uma quantidade não negligenciável de valores extremos (0 e 1) e que modelos usuais não são adequados para sua análise. Para esta situação propomos, por meio de um enfoque Bayesiano, modelos de regressão beta inflacionado de zeros e uns (BIZU) e modelos de regressão Tobit duplamente censurado adaptados nesse intervalo. Técnicas de diagnóstico e qualidade do ajuste também são discutidas. Apresentamos a análise desta estrutura de dados no contexto de série de tempo por meio da abordagem Bayesiana de modelos dinâmicos. Estudos de comportamento e previsão de séries de tempo foram explorados utilizando técnicas de Monte Carlo sequencial, conhecidas como filtro de partículas. Particularidades e competitividade entre as duas classes de modelos também foram discutidas. / This paper presents a study focused on observations in a limited interval , more specifically in [0,1] , such as rate and proportion data. In many practical cases this data structure has a considerable amount of extreme values (0 and 1) and usual classical models are not suitable for this type of data set. We propose two class of regression models to deal with this context: beta inflated of zeros and ones (BIZU) models and Tobit doubly censored models adapted in this interval. Fit quality and diagnostic techniques are also discussed. Time series of proportions are also developed through Bayesian dynamic models. Forecasting and behavioral analysis were explored using sequential Monte Carlo techniques, known as particle filters. Particularities and competitiveness between the two classes of models were also discussed as well.