Made available in DSpace on 2018-08-24T22:56:03Z (GMT). No. of bitstreams: 1 tese_9265_Tese Versão final 05 ficha.pdf: 6216026 bytes, checksum: f62b68a81860c6faf1c1cfdb4027e007 (MD5) Previous issue date: 2015-06-30 / O material particulado é um poluente atmosférico que provoca danos à saúde humana, dos animais e das plantas, afeta o clima além de ser causa potencial de incômodo quando se sedimenta em objetos, materiais e superfícies de uso cotidiano. Em regiões urbanas o material particulado se origina principalmente de fontes industriais, veiculares, suspensão do solo e construção civil que por meio dos efeitos do vento (direção e velocidade) promovem a dispersão e sedimentação das partículas. Esta tese estuda e avalia a percepção do incômodo causada por material particulado em regiões metropolitanas e industrializadas a fim de identificar os fatores determinantes do incômodo percebido e sua relação com níveis de material particulado (PM10, PTS e Partículas Sedimentadas). Os dados foram coletados por meio de pesquisas de opinião realizadas face a face e por telefone no período de 2011 a 2014 assim como dados de concentração dos poluentes medidos nas estações fixas de monitoramento da qualidade do ar. Os dados foram analisados por meio da aplicação de técnicas estatística multivariada, análise de correspondência múltipla, análise de componentes principais e regressão logística simples e múltipla. Os resultados mostraram que aproximadamente 90% dos respondentes estão incomodados com a poluição do ar, principalmente pela presença de poeira. De forma geral as variáveis indicadas como determinantes do incômodo são, importância da qualidade do ar, percepção do risco industrial, percepção da poluição, ocorrência de problemas na saúde, gênero e idade. Na análise entre os relatos de incômodo obtidos na pesquisa face a face e a concentração de PM10 e PTS para as sub-regiões da RGV observa-se probabilidade significativa de relatos de incômodo mesmo quando não expostos à concentração desses poluentes, indicando que o incômodo está muito mais relacionado à percepção da poeira. Esta hipótese se confirma na análise entre a percepção do incômodo relatada na pesquisa painel e as taxas de deposição de partículas medidas mensalmente. A partir daí foi possível estimar o percentual de indivíduos dado os aumentos gradativos na taxa de partículas sedimentadas para cada sub-região. Estes resultados contribuem cientifica e socialmente no sentido de fornecer um guia com diretrizes para subsidiar a definição de um padrão de qualidade do ar para o incômodo causado por partículas sedimentáveis na região metropolitana da Grande Vitória. Finalmente, por meio de uma análise do efeito combinado de diferentes formas de medir o material particulado foi possível estimar parâmetros para cálculo do risco relativo de incômodo para cada poluente e concluir que o valor mais preciso entre os riscos relativos estimados se refere às partículas sedimentadas.

Dirichlet process mixture (DPM) has been widely used as exible prior in nonparametric Bayesian literature, and Weighted Dirichlet process mixture (WDPM) can be viewed as extension of DPM which relaxes model distribution assumptions. Meanwhile, WDPM requires to set weight functions and can cause extra computation burden. In this dissertation, we develop more efficient and exible WDPM approaches under three research topics. The first one is semiparametric cubic spline regression where we adopt a nonparametric prior for error terms in order to automatically handle heterogeneity of measurement errors or unknown mixture distribution, the second one is to provide an innovative way to construct weight function and illustrate some decent properties and computation efficiency of this weight under semiparametric stochastic volatility (SV) model, and the last one is to develop WDPM approach for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model (as an alternative approach for SV model) and propose a new model evaluation approach for GARCH which produces easier-to-interpret result compared to the canonical marginal likelihood approach. In the first topic, the response variable is modeled as the sum of three parts. One part is a linear function of covariates that enter the model parametrically. The second part is an additive nonparametric model. The covariates whose relationships to response variable are unclear will be included in the model nonparametrically using Lancaster and Šalkauskas bases. The third part is error terms whose means and variance are assumed to follow non-parametric priors. Therefore we denote our model as dual-semiparametric regression because we include nonparametric idea for both modeling mean part and error terms. Instead of assuming all of the error terms follow the same prior in DPM, our WDPM provides multiple candidate priors for each observation to select with certain probability. Such probability (or weight) is modeled by relevant predictive covariates using Gaussian kernel. We propose several different WDPMs using different weights which depend on distance in covariates. We provide the efficient Markov chain Monte Carlo (MCMC) algorithms and also compare our WDPMs to parametric model and DPM model in terms of Bayes factor using simulation and empirical study. In the second topic, we propose an innovative way to construct weight function for WDPM and apply it to SV model. SV model is adopted in time series data where the constant variance assumption is violated. One essential issue is to specify distribution of conditional return. We assume WDPM prior for conditional return and propose a new way to model the weights. Our approach has several advantages including computational efficiency compared to the weight constructed using Gaussian kernel. We list six properties of this proposed weight function and also provide the proof of them. Because of the additional Metropolis-Hastings steps introduced by WDPM prior, we find the conditions which can ensure the uniform geometric ergodicity of transition kernel in our MCMC. Due to the existence of zero values in asset price data, our SV model is semiparametric since we employ WDPM prior for non-zero values and parametric prior for zero values. On the third project, we develop WDPM approach for GARCH type model and compare different types of weight functions including the innovative method proposed in the second topic. GARCH model can be viewed as an alternative way of SV for analyzing daily stock prices data where constant variance assumption does not hold. While the response variable of our SV models is transformed log return (based on log-square transformation), GARCH directly models the log return itself. This means that, theoretically speaking, we are able to predict stock returns using GARCH models while this is not feasible if we use SV model. Because SV models ignore the sign of log returns and provides predictive densities for squared log return only. Motivated by this property, we propose a new model evaluation approach called back testing return (BTR) particularly for GARCH. This BTR approach produces model evaluation results which are easier to interpret than marginal likelihood and it is straightforward to draw conclusion about model profitability by applying this approach. Since BTR approach is only applicable to GARCH, we also illustrate how to properly cal- culate marginal likelihood to make comparison between GARCH and SV. Based on our MCMC algorithms and model evaluation approaches, we have conducted large number of model fittings to compare models in both simulation and empirical study. / Ph. D.