A distribuição beta é apropriada para analisar dados de variáveis medidas no intervalo (0, 1), como taxas e proporções, como por exemplo a proporção de severidade de doenças em plantas. Portanto, dados que são pares observações de taxas e proporções, naturalmente pensa-se numa distribuição beta bivariada com suporte (0, 1)2. O objetivo deste trabalho constitui-se em encontrar a melhor distribuição beta bivariada na literatura para este caso e, além disso, tentar encontrar estimadores para seus parâmetros, a fim de verificar se esta distribuição escolhida se ajusta bem aos dados. Foi criada uma metodologia para a estimação dos parâmetros, utilizando aquela distribuição que consideramos a mais adequada. Posteriormente foram feitas simulações para avaliar a qualidade desses estimadores e, por fim, foram utilizados três bancos de dados com a finalidade de exemplificar esta metodologia. / Beta distribution is suitable for analyzing variable data measured in the range (0, 1), as rates and proportions, such as the proportion of disease severity in plants. Therefore, data that are paired observations rates and proportions naturally thinks in a bivariate distribution beta supported (0, 1)2. The objective of this work is on finding the best beta bivariate distribution in the literature for this case and, furthermore, try to find estimators for its parameters in order to verify that this chosen distribution fits the data well. A methodology was created for the estimation of parameters using that distribution we consider the most appropriate. Later simulations were performed to evaluate the quality of these estimators and, finally, we use three databases in order to illustrate this methodology.

A distribuição beta é apropriada para analisar dados de variáveis medidas no intervalo (0, 1), como taxas e proporções, como por exemplo a proporção de severidade de doenças em plantas. Portanto, dados que são pares observações de taxas e proporções, naturalmente pensa-se numa distribuição beta bivariada com suporte (0, 1)2. O objetivo deste trabalho constitui-se em encontrar a melhor distribuição beta bivariada na literatura para este caso e, além disso, tentar encontrar estimadores para seus parâmetros, a fim de verificar se esta distribuição escolhida se ajusta bem aos dados. Foi criada uma metodologia para a estimação dos parâmetros, utilizando aquela distribuição que consideramos a mais adequada. Posteriormente foram feitas simulações para avaliar a qualidade desses estimadores e, por fim, foram utilizados três bancos de dados com a finalidade de exemplificar esta metodologia. / Beta distribution is suitable for analyzing variable data measured in the range (0, 1), as rates and proportions, such as the proportion of disease severity in plants. Therefore, data that are paired observations rates and proportions naturally thinks in a bivariate distribution beta supported (0, 1)2. The objective of this work is on finding the best beta bivariate distribution in the literature for this case and, furthermore, try to find estimators for its parameters in order to verify that this chosen distribution fits the data well. A methodology was created for the estimation of parameters using that distribution we consider the most appropriate. Later simulations were performed to evaluate the quality of these estimators and, finally, we use three databases in order to illustrate this methodology.

In this thesis, we suggest a goodness-ofit test for semi-parametric copula models. We extended the pseudo in-and-out-sample (PIOS) test proposed in [17], which is based on the PIOS test in [28]. The PIOS test is constructed by comparing the pseudo "in-sample" likelihood and pseudo "out-of-sample" likelihood. Our contribution is twoold. First, we use the approximate test statistics instead of the exact test statistics to alleviate the computational burden of calculating the test statistics. Secondly, we propose a parametric bootstrap procedure to approximate the distribution of the test statistic. Unlike the nonparametric bootstrap which resamples from the original data, the parametric procedure resamples the data from the copula model under the null hypothesis. We conduct simulation studies to investigate the performance of the approximate test statistic and parametric bootstrap. The results show that the parametric bootstrap presents higher test power with a well-controlled type I error compared to the nonparametric bootstrap.

Ocean wave energy is considered to be one of the important potential renewable energy resources for sustainable development. Various wave energy converter technologies have been proposed to harvest the energy from ocean waves. This thesis is based on the linear generator wave energy converter developed at Uppsala University. The research in this thesis focuses on the foundation optimization and the power absorption optimization of the wave energy converters and on the wave climate modelling at the Lysekil wave converter test site. The foundation optimization study of the gravity-based foundation of the linear wave energy converter is based on statistical analysis of wave climate data measured at the Lysekil test site. The 25 years return extreme significant wave height and its associated mean zero-crossing period are chosen as the maximum wave for the maximum heave and surge forces evaluation. The power absorption optimization study on the linear generator wave energy converter is based on the wave climate at the Lysekil test site. A frequency-domain simplified numerical model is used with the power take-off damping coefficient chosen as the control parameter for optimizing the power absorption. The results show a large improvement with an optimized power take-off damping coefficient adjusted to the characteristics of the wave climate at the test site. The wave climate modelling studies are based on the wave climate data measured at the Lysekil test site. A new mixed distribution method is proposed for modelling the significant wave height. This method gives impressive goodness of fit with the measured wave data. A copula method is applied to the bivariate joint distribution of the significant wave height and the wave period. The results show an excellent goodness of fit for the Gumbel model. The general applicability of the proposed mixed-distribution method and the copula method are illustrated with wave climate data from four other sites. The results confirm the good performance of the mixed-distribution and the Gumbel copula model for the modelling of significant wave height and bivariate wave climate.

[pt] Nesta tese, consideramos um modelo de espaço de estado bivariado para dados de contagem. A abordagem usada para resolver integrais não-analíticas que se apresentam no modelo é uma natural extensão da metodologia proposta por Durbin e Koopman - (DK), no sentido de que o Modelo Gaussiano Aproximador deve possuir algumas matrizes de covariâncias diagonais. Esta modificação traz a vantagem de viabilizar o uso do tratamento univariado para séries multivariadas com as recursões de Kalman, o qual, como se sabe, é mais eficiente do que o tratamento usual e facilita o uso de inicializações exatas destas mesmas recursões. O vetor de estado do modelo proposto é definido usando-se abordagem estrutural, onde os elementos do vetor de estado têm interpretação direta como tendência e sazonalidade. Apresentamos exemplos simulados e reais para ilustrar o modelo. / [en] In this thesis we consider a state space model for bivariate observations of count data. The approach used to solve the non analytical integrals that appears as the solution of the resulting non-Gaussian filter is a natural extension of the methodology advocated by Durbin and Koopman (DK). In our approach the aproximated Gaussian Model (AGM), has a diagonal Covariance matrix, while in the original DK, this is a full matrix. This modification make it possible to use univariate Kalman recursoes to construct the AGM, resulting in a computationally more efficient solution for the estimation of a Bivariate Poisson model. This also facilitates the use of exact initialization of those recursions. The state vector is specified using the structural approach, where the state elements are components which have direct interpretation, such as trend and seasonals. In our bivariate set up the dependence between the bivariate vector of time series is accomplished by use of common components which drive both series. We present both simulation and real life examples illustrating the use of our model.