A inferência Bayesiana está cada vez mais dependente de algoritmos de simulação estocástica, e sua eficiência está diretamente relacionada à eficiência do algoritmo considerado. Uma prática bastante utilizada é a introdução de variáveis auxiliares para obtenção de formas conhecidas para as distribuições {\\it a posteriori} condicionais completas, as quais facilitam a implementação do amostrador de Gibbs. No entanto, a introdução dessas variáveis pode produzir algoritmos onde os valores simulados são fortemente correlacionados, fato esse que prejudica a convergência. O agrupamento das quantidades desconhecidas em blocos, de tal maneira que seja viável a simulação conjunta destas quantidades, é uma alternativa para redução da autocorrelação, e portanto, ajuda a melhorar a eficiência do procedimento de simulação. Neste trabalho, apresentamos propostas de simulação em blocos no contexto de modelos de regressão binária com o uso de variáveis auxiliares. Três classes de funções de ligação são consideradas: probito, logito e probito-assimétrico. Para as duas primeiras apresentamos e implementamos as propostas de atualização conjunta feitas por Holmes e Held (2006). Para a ligação probito-assimétrico propomos quatro diferentes maneiras de construir os blocos, e comparamos estes algoritmos através de duas medidas de eficiência (distância média Euclidiana entre atualizações e tamanho efetivo da amostra). Concluímos que os algoritmos propostos são mais eficientes que o convencional (sem blocos), sendo que um deles proporcionou ganho superior a 160\\% no tamanho efetivo da amostra. Além disso, discutimos uma etapa bastante importante da modelagem, denominada análise de resíduos. Nesta parte adaptamos e implementamos os resíduos propostos para a ligação probito para os modelos logístico e probito-assimétrico. Finalmente, utilizamos os resíduos propostos para verificar a presença de observações discrepantes em um conjunto de dados simulados. / The Bayesian inference is getting more and more dependent of stochastic simulation algorithms, and its efficiency is directly related with the efficiency of the considered algorithm. The introduction of auxiliary variables is a technique widely used for attainment of the full conditional distributions, which facilitate the implementation of the Gibbs sampling. However, the introduction of these auxiliary variables can produce algorithms with simulated values highly correlated, this fact harms the convergence. The grouping of the unknow quantities in blocks, in such way that the joint simulation of this quantities is possible, is an alternative for reduction of the autocorrelation, and therefore, improves the efficiency of the simulation procedure. In this work, we present proposals of simulation using the Gibbs block sampler in the context of binary response regression models using auxiliary variables. Three class of links are considered: probit, logit and skew-probit. For the two first we present and implement the scheme of joint update proposed by Holmes and Held (2006). For the skew-probit, we consider four different ways to construct the blocks, and compare these algorithms through two measures of efficiency (the average Euclidean update distance between interactions and effective sample size). We conclude that the considered algorithms are more efficient than the conventional (without blocks), where one of these leading to around 160\\% improvement in the effective sample size. Moreover, we discuss one important stage of the modelling, called residual analysis. In this part we adapt and implement residuals considered in the probit model for the logistic and skew-probit models. For a simulated data set we detect the presence of outlier used the residuals proposed here for the different models.

Submitted by Franciele Moreira (francielemoreyra@gmail.com) on 2018-03-28T12:09:11Z No. of bitstreams: 2 Dissertação - Danúbia Rodrigues da Cunha - 2018.pdf: 7944363 bytes, checksum: f0229888d666e1d8367f8c31ee27a238 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-03-29T11:30:21Z (GMT) No. of bitstreams: 2 Dissertação - Danúbia Rodrigues da Cunha - 2018.pdf: 7944363 bytes, checksum: f0229888d666e1d8367f8c31ee27a238 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-03-29T11:30:21Z (GMT). No. of bitstreams: 2 Dissertação - Danúbia Rodrigues da Cunha - 2018.pdf: 7944363 bytes, checksum: f0229888d666e1d8367f8c31ee27a238 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-08 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this work, bivariate regression models based on the bivariate normal, t and Birnbaum-Saunders distributions are used to analyze labor market data. In special, the objective is to model the dependent variable of the Mincerian earnings equation separately, namely, the variable hourly earnings (which is obtained by dividing gross monthly earnings by hours worked) is modeled in two parts, earnings and hours worked. The bivariate regression models are used to model these two parts in order to try to capture the correlation between them and the different effects, that is, remuneration or premium for labor effort, and the labor supply or the time that the worker offers to the market. In order to accomplish this, data from the Brazilian National Household Sample Survey (PNAD) for the years 2013, 2014 and 2015 are used. The parameters of the models are estimated using the maximum likelihood method. The results show that the bivariate regression model based on the bivariate t distribution has the best fit for the data, and that the presence of correlation between earnings and hours worked indicates that the bivariate model is more adequate than the univariate model. / Nessa dissertação, modelos de regressão bivariada baseados nas distribuições bivariadas normal, t e Birnbaum-Saunders são usados para analisar dados do mercado de trabalho. Em especial, o objetivo é modelar a variável dependente da equação de rendimento minceriana de forma separada, ou seja, o rendimento-hora é modelado em duas partes, rendimento e horas trabalhadas. Os modelos de regressão bivariada são utilizados para modelar essas duas partes de forma a tentar captar a correlação entre elas e os distintos efeitos, ou seja, remuneração ou prêmio pelo esforço desprendido pela mão de obra, e oferta de trabalho ou o tempo que o trabalhador disponibiliza ao mercado. Para tal, usa-se dados da Pesquisa Nacional por Amostra de Domicílios (PNAD) para os anos de 2013, 2014 e 2015. Os parâmetros dos modelos são estimados usando o método da máxima verossimilhança. Os resultados mostram que o modelo de regressão bivariada baseada na distribuição bivariada t tem o melhor ajuste para os dados, e que a presença de correlação entre rendimento e horas trabalhadas indica que o modelo bivariado é mais adequado que o univariado.

In the agricultural state of Iowa, water quality research is of great importance for monitoring and managing the health of aquatic systems. Among many water quality parameters, water temperature is a critical variable that governs the rates of chemical and biological processes which affect river health. The main objective of this thesis is to develop a real-time high resolution predictive stream temperature model for the entire state of Iowa. A statistical model based solely on the water-air temperature relationship was developed using logistic regression approach. With hourly High Resolution Rapid Refresh (HRRR) air temperature estimations, the implemented stream temperature model produces current state-wide estimations. The results are updated hourly in real-time and presented on a web-based visualization platform: the Iowa Water Quality Information System, Beta version (IWQIS Beta). Streams of 4th order and up are color-coded according to the estimated temperatures. Hourly forecasts for lead time of up to 18 hours are also available. A model was developed separately for spring (March to May), summer (June to August), and autumn (September to November) seasons. 2016 model estimation results generate less than 3 °C average RMSE for the three seasons, with a summer season RMSE of below 2 °C. The model is transferrable to basins of different catchment sizes within the state of Iowa and requires hourly air temperature as the only input variable. The product will assist Iowa water quality research and provide information to support public management decisions.

The objective of the present study is to investigate key aspects of ovarian and breast cancers, which are two main causes of mortality among women. Identification of the true behavior of survivorship and influential risk factors is essential in designing treatment protocols, increasing disease awareness and preventing possible causes of disease. There is a commonly held belief that African Americans have a higher risk of cancer mortality. We studied racial disparities of women diagnosed with ovarian cancer on overall and disease-free survival and found out that there is no significant difference in the survival experience among the three races: Whites, African Americans and Other races. Tumor sizes at diagnosis among the races were significantly different, as African American women tend to have larger ovarian tumor sizes at the diagnosis. Prognostic models play a major role in health data research. They can be used to estimate adjusted survival probabilities and absolute and relative risks, and to determine significantly contributing risk factors. A prognostic model will be a valuable tool only if it is developed carefully, evaluating the underlying model assumptions and inadequacies and determining if the most relevant model to address the study objectives is selected. In the present study we developed such statistical models for survival data of ovarian and breast cancers. We found that the histology of ovarian cancer had risk ratios that vary over time. We built two types of parametric models to estimate absolute risks and survival probabilities and to adjust the time dependency of the relative risk of Histology. One parametric model is based on classical probability distributions and the other is a more flexible parametric model that estimates the baseline cumulative hazard function using spline functions. In contrast to women diagnosed with ovarian cancer, women with breast cancer showed significantly different survivorship among races where Whites had a poorer overall survival rate compared to African Americans and Other races. In the breast cancer study, we identified that age and progesterone receptor status have time dependent hazard ratios and age and tumor size display non-linear effects on the hazard. We adjusted those non-proportional hazards and non-linear effects by using an extended Cox regression model in order to generate more meaningful interpretations of the data.

The primary objective of the study is to evaluate safety performances of different exit ramps used in Florida and nationally. More specific, the research objectives include the following two parts: (1) to evaluate the impacts of different exit ramp types on safety performance for freeway diverge areas; and (2) to identify the different factors contributing to the crashes happening on the exit ramp sections. To achieve the research objectives, the research team investigated crash history at 424 sites throughout Florida. The study area includes two parts, the freeway diverge area and the exit ramp sections. For the freeway diverge areas, exit ramp types were defined based on the number of lanes used by vehicular traffic to exit freeways. Four exit ramp types were considered here including single-lane exit ramps (Type 1), sing-lane exit ramps without a taper (Type 2), two-lane exit ramps with an optional lane (Type 3), and two-lane exit ramps without an optional lane (Type 4). For the exit ramp sections, four ramp configurations, including diamond, out connection, free-flow loop and parclo loop, were considered. Cross-sectional comparisons were conducted to compare crash frequency, crash rate, crash severity and crash types between different exit ramp groups. Crash predictive models were also built to quantify the impacts of various contributing factors. On the freeway diverge areas, it shows that Type 1 exit ramp has the best safety performance in terms of the lowest crash frequency and crash rate. The crash prediction model shows that for one-lane exit ramp, replacing a Type 1 with a Type 2 will increase crash counts at freeway diverge areas by 15.57% while replacing a Type 3 with a Type 4 will increase crash counts by 10.80% for two-lane ramps. On the exit ramp sections, the out connection ramps appear to have the lowest average crash rate than the other three. The crash predictive model shows that replacing an out connection exit ramp with a diamond, free-flow, and parclo loop will increase crashes counts by 26.90%, 68.47% and 48.72% respectively. The results of this study will help transportation decision makers develop tailored technical guidelines governing the selection of the optimum design combinations on freeway diverge areas and exit ramp sections.

<p>With the development of the financial markets, the foreign exchange market has become more and more important for investors. The daily volume of business dealt with on the foreign exchange markets in 1998 was estimated to be over $2.5 trillion dollars (the daily volume on New York Stock Exchanges is about $20 billion). Today (2006) it may be about $5 trillion dollars. More and more people notice the foreign exchange market, and more and more sophisticated investors research such markets. The purpose of this thesis is to compare different methods to forecast the exchange rate of the money pair AUD/JPY. Firstly we studied the relationship between the AUD/JPY exchange rate and some economic fundamentals by using a regression model. Secondly, we tested whether the AUD/JPY exchange rate had any relationship with its historical records by using an ARIMA model. Finally, we compared the two model forecasting performance. A secondary purpose is to test whether the Market Efficiency Hypothesis works on the money pair AUD/JPY. In the study, data from January 1986 to June 2006 were chosen. To test which method produces better forecasts, we chose data from January 1986 to December 2002 to build up the prediction functions. Then we used the data from January 2003 to 2006 June to evaluate which predicting method was closer to the reality. In the comparison of the forecasting performances, two approaches dealing with the unknown future fundamentals were used. Firstly we assumed that we could do perfect predictions of these regressors, that was, our predictions of these regressors were the same as the actual future outcomes. So we put the real data for the fundamentals from January 2003 to June 2006 into the regression function. Secondly we assumed that we were in real life situation, and we had to predict the regressors first in order to get the predictions of the exchange rate. The results of the comparison were that the AUD/JPY exchange rate could to some extent be predictable, and that the predictions by the ARIMA model were more accurate.</p>

<p>Given the question of how to further decrease childhood mortality and attain the fourth MDG in Bangladesh, the determinants of childhood mortality and successful health intervention programmes in a rural area of Bangladesh are examined in this paper. The binominal logit regression analysis, on Matlab HDSS data from 2001 to 2005, indicates that the child’s birth order, outcome of mother’s previous pregnancy, mother’s age, mother’s education, economic condition of the household, immunization, and place of delivery are important determining factors of childhood mortality. Interview discussions show that the delivery of health services is a determining factor for successful health intervention programmes. It is worth to note that childhood mortality levels are no longer significantly lower in the treatment area of Matlab. Furthermore, the intervention programmes in the area require continuous reform, especially in the fields of birth assistance and injury prevention.</p>

Given the question of how to further decrease childhood mortality and attain the fourth MDG in Bangladesh, the determinants of childhood mortality and successful health intervention programmes in a rural area of Bangladesh are examined in this paper. The binominal logit regression analysis, on Matlab HDSS data from 2001 to 2005, indicates that the child’s birth order, outcome of mother’s previous pregnancy, mother’s age, mother’s education, economic condition of the household, immunization, and place of delivery are important determining factors of childhood mortality. Interview discussions show that the delivery of health services is a determining factor for successful health intervention programmes. It is worth to note that childhood mortality levels are no longer significantly lower in the treatment area of Matlab. Furthermore, the intervention programmes in the area require continuous reform, especially in the fields of birth assistance and injury prevention.

With the development of the financial markets, the foreign exchange market has become more and more important for investors. The daily volume of business dealt with on the foreign exchange markets in 1998 was estimated to be over $2.5 trillion dollars (the daily volume on New York Stock Exchanges is about $20 billion). Today (2006) it may be about $5 trillion dollars. More and more people notice the foreign exchange market, and more and more sophisticated investors research such markets. The purpose of this thesis is to compare different methods to forecast the exchange rate of the money pair AUD/JPY. Firstly we studied the relationship between the AUD/JPY exchange rate and some economic fundamentals by using a regression model. Secondly, we tested whether the AUD/JPY exchange rate had any relationship with its historical records by using an ARIMA model. Finally, we compared the two model forecasting performance. A secondary purpose is to test whether the Market Efficiency Hypothesis works on the money pair AUD/JPY. In the study, data from January 1986 to June 2006 were chosen. To test which method produces better forecasts, we chose data from January 1986 to December 2002 to build up the prediction functions. Then we used the data from January 2003 to 2006 June to evaluate which predicting method was closer to the reality. In the comparison of the forecasting performances, two approaches dealing with the unknown future fundamentals were used. Firstly we assumed that we could do perfect predictions of these regressors, that was, our predictions of these regressors were the same as the actual future outcomes. So we put the real data for the fundamentals from January 2003 to June 2006 into the regression function. Secondly we assumed that we were in real life situation, and we had to predict the regressors first in order to get the predictions of the exchange rate. The results of the comparison were that the AUD/JPY exchange rate could to some extent be predictable, and that the predictions by the ARIMA model were more accurate.

The role of the spatial representation in the relation between chronic exposure to NO2 and respiratory health outcomes is studied through a spatial approach encompassing three conceptual components: the geography of susceptibility, the geography of exposure and the geography of risk. A spatially explicit methodology that defined natural neighbourhoods for the city of Ottawa is presented; it became the geography of analysis in this research. A LUR model for Ottawa is developed to study the geography of exposure. Model sensitivity to the spatial representation of population showed that dasymetric population mapping did not provide significant improvements to the LUR model over population at the dissemination block level. However, both the former were significantly better than population represented at the dissemination area. Spatial representation in the geography of exposure was also evaluated by comparing four kriging and cokriging interpolation models to the LUR. Geostatistically derived NO2 concentration maps were weakly correlated with LUR model results. The relationship between mean NO2 concentrations and respiratory health outcomes was assessed within the natural neighbourhoods. We find a statistically significant association between NO2 concentrations and respiratory health outcomes as measured by global bivariate Moran’s I. However, for regression model building, NO2 had to be forced into the model, demonstrating that NO2 is not one of the main contributing variables to respiratory health outcomes in Ottawa. The results point toward the importance of the socioeconomic status on the health condition of individuals. Finally, the role of spatial representation was assessed using three different spatial structures, which also permitted to better understand the role of the modifiable areal unit problem (MAUP) in the study of the relationship between exposure to NO2 and health. The results confirm that NO2 concentration is not a major contributing factor to the respiratory health in Ottawa but clearly demonstrate the implications that the use of opportunistic administrative boundaries can have on results of exposure studies. The effects of the MAUP, the scale effect and the zoning effect, were observed indicating that a spatial structure that embodies the scale of major social processes behind the health condition of individuals should be used when possible.