The Birnbaum-Saunders (BS) distribution is the most popular model used to describe lifetime process under fatigue. Throughout the years, this distribution has received a wide ranging of applications, demanding some more flexible extensions to solve more complex problems. One of the most well-known extensions of the BS distribution is the generalized Birnbaum- Saunders (GBS) family of distributions that includes the Birnbaum-Saunders special-case (BSSC) and the Birnbaum-Saunders generalized t (BSGT) models as special cases. Although the BS-SC distribution was previously developed in the literature, it was never deeply studied and hence, in this thesis, we provide a full Bayesian study and develop a tool to generate random numbers from this distribution. Further, we develop a very flexible regression model, that admits different degrees of skewness and kurtosis, based on the BSGT distribution using the generalized additive models for location, scale and shape (GAMLSS) framework. We also introduce a new extension of the BS distribution called the Birnbaum-Saunders power (BSP) family of distributions, which contains several special or limiting cases already published in the literature, including the GBS family. The main feature of the new family is that it can produce both unimodal and bimodal shapes depending on its parameter values. We also introduce this new family of distributions into the GAMLSS framework, in order to model any or all the parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. Throughout this thesis we present five different applications in real data sets in order to illustrate the developed theoretical results. / A distribuição Birnbaum-Saunders (BS) é o modelo mais popular utilizado para descrever processos de fadiga. Ao longo dos anos, essa distribuição vem recebendo aplicações nas mais diversas áreas, demandando assim algumas extensões mais flexíveis para resolver problemas mais complexos. Uma das extensões mais conhecidas na literatura é a família de distribuições Birnbaum-Saunders generalizada (GBS), que inclui as distribuições Birnbaum-Saunders casoespecial (BS-SC) e Birnbaum-Saunders t generalizada (BSGT) como modelos especiais. Embora a distribuição BS-SC tenha sido previamente desenvolvida na literatura, nunca foi estudada mais profundamente e, assim, nesta tese, um estudo bayesiano é desenvolvido acerca da mesma além de um novo gerador de números aleatórios dessa distribuição ser apresentado. Adicionalmente, um modelo de regressão baseado na distribuição BSGT é desenvolvido utilizando-se os modelos aditivos generalizados para locação, escala e forma (GAMLSS), os quais apresentam grande flexibilidade tanto para a assimetria como para a curtose. Uma nova extensão da distribuição BS também é apresentada, denominada família de distribuições Birnbaum-Saunders potência (BSP), que contém inúmeros casos especiais ou limites já publicados na literatura, incluindo a família GBS. A principal característica desta nova família é que ela é capaz de produzir formas tanto uni como bimodais dependendo do valor de seus parâmetros. Esta nova família também é introduzida na estrutura dos modelos GAMLSS para fornecer uma ferramenta capaz de modelar todos os parâmetros da distribuição como funções lineares e/ou não-lineares suavizadas de variáveis explicativas. Ao longo desta tese são apresentadas cinco diferentes aplicações em conjuntos de dados reais para ilustrar os resultados teóricos obtidos.

MSc (Statistics) / Department of Statistics / Generally, short term load forecasting is essential for any power generating utility. In this dissertation the main objective was to develop short term load forecasting models for the peak demand periods (i.e. from 18:00 to 20:00 hours) in South Africa using. Quantile semi-parametric additive models were proposed and used to forecast electricity demand during peak hours. In addition to this, forecasts obtained were then used to nd an optimal number of generating units to commit (switch on or o ) daily in order to produce the required electricity demand at minimal costs. A mixed integer linear programming technique was used to nd an optimal number of units to commit. Driving factors such as calendar e ects, temperature, etc. were used as predictors in building these models. Variable selection was done using the least absolute shrinkage and selection operator (Lasso). A feasible solution to the unit commitment problem will help utilities meet the demand at minimal costs. This information will be helpful to South Africa's national power utility, Eskom. / NRF

Made available in DSpace on 2011-05-04T12:42:00Z (GMT). No. of bitstreams: 0 Previous issue date: 2009 / (...) O objetivo desta tese foi modelar os fatores de risco associados à ocorrência de leptospirose urbana em diferentes contextos, com especial atenção para aspectos espaciais e temporais. Foram utilizadas técnicas de modelagem tais como, modelos generalizados aditivos e mistos. Também explorou-se técnicas de detecção de aglomerados espaço-temporais. (...) / Leptospirosis, a disease caused by pathogenic spirochete of the genus Leptospira, is one of the most widespread zoonoses in the world, considered a major public health problem associated with the lack of sanitation and poverty. It is endemic in Brazil, data from surveillance show that outbreaks of leptospirosis occur as cyclical annual epidemics during rainfalls. The aim of this thesis was modeling the risk factors associated with the occurrence of leptospirosis in di erent urban contexts, with particular attention to spatial and temporal aspects. We used some modeling techniques such as generalized additive and mixed models. Techniques for detection space-time clusters were also explored. This thesis has prioritized the use of free softwares - R, ubuntu linux operating system, LATEX , SatScan (this is not open source but free). This thesis was prepared in the form of three articles. In the rst article is presented a spatio-temporal analysis of leptospirosis cases occurrence in Rio de Janeiro between 1997 and 2002. Using the detection of space-time clusters - \outbreaks" method - were statistically signi cant only cluster ocorred in 1997 and 1998. Generalized Linear Mixed Models were used to evaluate the risk factors associated with the occurrence of cases that belonged to outbreaks in endemic cases. The cases belonging to the outbreaks are associated with the occurrence of rainfall over 4 mm (OR, 3.71; 95% CI, 1.83 - 7.51). There were no signi cant associations with socioeconomic covariates, in other words, being endemic or epidemic leptospirosis occurs in the same population. The second and third articles examined a seroprevalence survey and seroconversion cohort conducted in Pau da Lima community, Salvador, Bahia. In both Generalized Additive Models were used to t the exposure variables both in individuals and peridomicile context, as well as to estimate the spatial area of leptospirosis risk. The signi cant variables were: gender, age, presence of rats in the peridomicile, domicile near a trash collectin or an open sewer and domicile altitude above sea level. Studies show that individual and contextual variables explain much of the spatial variability of leptospirosis, but there are still factors that were not measured in the studies but which should be investigated. The maps of risk of seroprevalence and seroconversion show distinct regions where the spatial e ect is signi cantly di erent from the global average. It is still lack for a more robust integration between the professionals who develop and operate the GIS, epidemiologists and biostatistics. This integration represents an important advance enabling the development and use of these techniques in Public Health support. The study of prevalence and incidence of endemic areas, in the leptospirosis context, it is very complex and still grow up. The reunion of professional specialists from several areas of human knowledge (eg, clinicians, epidemiologists, geographers, biologists, statisticians, engineers, etc.), it is essential to advance the knowledge about the disease and their relationship to social inequality and environmental well to contribute to the creation of efficient and e ective measures to control endemic diseases.

MSc (Statistics) / Department of Statistics / Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electricity demand at 19:00 and 20:00 including daily peak electricity demand. Least absolute shrinkage and selection operator (Lasso) and Lasso via hierarchical interactions were used for variable selection to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, this way also over tting is reduced. The parameters of the developed models were estimated using restricted maximum likelihood and penalized regression. The best models were selected based on smallest values of the Akaike information criterion (AIC), Bayesian information criterion (BIC) and Generalized cross validation (GCV) along with the highest Adjusted R2. Forecasts from best models with and without tensor product interactions were evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). Operational forecasting was proposed to forecast the demand at hour 19:00 with unknown predictor variables. Empirical results from this study show that modelling hours individually during the peak period results in more accurate peak forecasts compared to forecasting daily peak electricity demand. The performance of the proposed models for hour 19:00 were compared and the generalized additive model with tensor product interactions was found to be the best tting model. / NRF

Management and conservation of populations of animals requires information on where they are, why they are there, and where else they could be. These objectives are typically approached by collecting data on the animals’ use of space, relating these to prevailing environmental conditions and employing these relations to predict usage at other geographical regions. Technical advances in wildlife telemetry have accomplished manifold increases in the amount and quality of available data, creating the need for a statistical framework that can use them to make population-level inferences for habitat preference and space-use. This has been slow-in-coming because wildlife telemetry data are, by definition, spatio-temporally autocorrelated, unbalanced, presence-only observations of behaviorally complex animals, responding to a multitude of cross-correlated environmental variables. I review the evolution of techniques for the analysis of space-use and habitat preference, from simple hypothesis tests to modern modeling techniques and outline the essential features of a framework that emerges naturally from these foundations. Within this framework, I discuss eight challenges, inherent in the spatial analysis of telemetry data and, for each, I propose solutions that can work in tandem. Specifically, I propose a logistic, mixed-effects approach that uses generalized additive transformations of the environmental covariates and is fitted to a response data-set comprising the telemetry and simulated observations, under a case-control design. I apply this framework to non-trivial case-studies using data from satellite-tagged grey seals (Halichoerus grypus) foraging off the east and west coast of Scotland, and northern gannets (Morus Bassanus) from Bass Rock. I find that sea bottom depth and sediment type explain little of the variation in gannet usage, but grey seals from different regions strongly prefer coarse sediment types, the ideal burrowing habitat of sandeels, their preferred prey. The results also suggest that prey aggregation within the water column might be as important as horizontal heterogeneity. More importantly, I conclude that, despite the complex behavior of the study species, flexible empirical models can capture the environmental relationships that shape population distributions.