Time-since-fire data are used to describe wildfire disturbances, the major disturbance type in the Boreal forest, over a landscape. These data can be used to calculate various parameters about wildfire disturbances, such as size, shape and severity. Collecting time-since-fire data is expensive and time consuming; the ability to derive it from existing forest inventory data would result in availability of fire data over larger areas. The objective of this thesis was to explore the use of forest inventory information for the prediction of time-since-fire data in the mixedwood boreal forests of Saskatchewan. Regression models were used to predict time-since-fire from forest inventory variables for each inventory polygon with a stand age. Non-water polygons with no stand age value were assigned values from neighbouring polygons, after splitting long polygons that potentially crossed many historic fire boundaries. This procedure filled gaps that prevented polygons from being grouped together in latter analysis. The predicted time-since-fire ages were used to generate wildfire parameters such as age-class distributions and fire cycle. Three methods were examined to group forest inventory polygons together to predict fire event polygons: simple partitions, hierarchical clustering, and spatially constrained clustering. The predicted fire event polygons were used to generate polygon size distribution wildfire metrics. I found that there was a relationship between time-since-fire and forest inventory variables at this study site, although the relationship was not strong. As expected, the strongest relationship was between the age of trees in a stand as indicated by the inventory and the time-since-fire. This relationship was moderately improved by including tree species composition, harvest modification value, and the ages of the surrounding polygons. Assigning no-age polygons neighbouring values and grouping the forest inventory polygons improved the predicted time-since-fire results when compared spatially to the observed time-since-fire data. However, a satisfactory method of comparing polygon shapes was not found, and the map outputs were highly dependent on the grouping method and parameters used. Overall it was found that forest inventory data did not have sufficient detail and accuracy to be used to derive high quality time-since-fire information.

A truncated sample consists of realizations of a pair of random variables (L, T) subject to the constraint that L ≤T. The major study interest with a truncated sample is to find the marginal distributions of L and T. Many studies have been done with the assumption that L and T are independent. We introduce a new way to specify a Cox model for a truncated sample, assuming that the truncation time is a predictor of T, and this causes the dependence between L and T. We develop an algorithm to obtain the adjusted risk sets and use the Kaplan-Meier estimator to estimate the Marginal distribution of L. We further extend our method to more practical situation, in which the Cox model includes other covariates associated with T. Simulation studies have been conducted to investigate the performances of the Cox model and the new estimators.

Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application.

Cox Proportional Hazard Model is one of the most popular tools used in the study of Survival Analysis. Empirical Likelihood (EL) method has been used to study the Cox Proportional Hazard Model. In recent work by Qin and Jing (2001), empirical likelihood based confidence region is constructed with the assumption that the baseline hazard function is known. However, in Cox’s regression model the baseline hazard function is unspecified. In this thesis, we re-formulate empirical likelihood for the vector of regression parameters by estimating the baseline hazard function. The EL confidence regions are obtained accordingly. In addition, Adjusted Empirical Likelihood (AEL) method is proposed. Furthermore, we conduct extensive simulation studies to evaluate the performance of the proposed empirical likelihood methods in terms of coverage probabilities by comparing with the Normal Approximation based method. The simulation studies show that all the three methods produce similar coverage probabilities.

This thesis deals with the appropriate handling of spatial data in general, and in particular in the framework of economic sciences. An overview of well known methods from the field of spatial statistics and spatial econometrics is given. Furthermore a special class of spatial objects is described, namely objects that are that far apart from all other observations in the dataset, that they are not connected to them anymore. Different treatments of such objects are suggested and their influence on the Moran's I test for spatial autocorrelation is analyzed in more detail. After this theoretical part some adequate spatial methods are applied to the well-known problem of R&D spillovers. The corresponding dataset is not obviously spatial, nevertheless spatial methods can be used. The spatial contiguity matrix is based on an economic distance measure instead of the commonly used geographic distances. Finally, optimal design theory and spatial analysis are combined via a new criterion. This criterion was developed to be able to take a potential spatial dependency of the data points into account. The aim is to find the best design points that show the same spatial dependence structure as the true population. (author's abstract)

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.

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

Construction projects in developing countries may suffer from time overruns, which are associated with cost overruns. This research project investigated both time and cost overruns on high-rise building projects in Dhaka, Bangladesh. Surprisingly, preliminary data analysis showed negligible cost overruns in comparison to time overruns. So, further analysis o f cost overruns was not considered in this thesis. This research project also investigated how the causes o f time-overruns can be mitigated. 72 time-overrun and 22 cost-overrun variables were identified through a literature review. These variables were taken as parameters and a personal interview survey was conducted with developers, consultants, contractors and project managers using semistructured questionnaire. A similar second survey was conducted using 22 measures, which can mitigate time- overruns. Data analysis involved the relative importance index to rank the variables, factors analysis to reduce variables to factors with minimum loss of data, stepwise regression to find links among factors in successive stages of construction process and multiple regression to explain delays in terms of factors. The main causes o f time-overruns were ‘cash flow’, ‘planning and scheduling deficiency’ and ‘design changes’. A scree graph identified 31 important variables that caused delays but factor analysis reduced these to 14 factors. Stepwise regression found no strong links among the factors to identify them as reasons for delay in successive stages of the construction project. A multiple regression model explained about 85% of the variance of the delays using eight factors. The main individual measures mitigating time-overruns were ‘improvement of cash flow’, ‘improvement o f communication and coordination among project participants’ and ‘development o f robust planning and scheduling instruments’. Factor analysis produced ten representative factors. Stepwise regression could not find strong links among factors mitigating time-overruns in successive stages of the construction project.

Made available in DSpace on 2016-06-02T20:06:03Z (GMT). No. of bitstreams: 1 1439.pdf: 2669525 bytes, checksum: 610b8a21f9390e878877d6a634cd8873 (MD5) Previous issue date: 2007-05-18 / This work involves a study of a regression model appropriated for situations which the response variable is measured in a continuous scale in the (0, 1) interval, as, for instance, taxes or proportions. The developed inferences were based on the Classic and Bayesian methodology. A discussion about the application of the beta regression model is presented. / Este trabalho compreende um estudo de um modelo de Regressão Beta adequado para situações em que a variável resposta é medida de forma contínua no intervalo (0, 1) como, por exemplo, dados de taxas ou proporções. As inferências desenvolvidas foram baseadas nas metodologias Clássica e Bayesiana. É apresentada uma discussão ampla sobre a aplicação do modelo de regressão beta a conjuntos de dados reais, o caso Charter Schools.

Este trabalho apresenta uma análise estatística de contagens multivariadas proveniente de várias populações através de modelos de regressão. Foram considerados casos onde os vetores respostas obedeçam às distribuições Poisson multivariada e Poisson log-normal multivariada. Esta distribuição admite correlação de ambos sinais entre componentes do vetor resposta, enquanto que as distribuições mais usuais para dados de contagens (como a Poisson multivariada) admitem apenas correlação positiva entre as componentes do vetor resposta. São discutidos métodos de estimação e testes de hipóteses sobre os parâmetros do modelo para o caso bivariado. Estes modelos de regressão foram aplicados a um conjunto de dados referentes a contagens de dois tipos de defeitos em 100 gramas de fibras têxteis de quatro máquinas craqueadeiras, sendo duas de um fabricante e as outras de um segundo fabricante. Os resultados obtidos nos diferentes modelos de regressão foram comparados. Para estudar o comportamento das estimativas dos parâmetros de uma distribuição Poisson Log-Normal, amostras foram simuladas segundo esta distribuição. / Regression models are presented to analyse multivariate counts from many populations. Due to the random vector characteristic, we consider two classes of probability models: Multivariate Poisson distribution and Multivariate Poisson Log-Normal distribution. The last distribution admits negative and positive correlations between two components of a random vector under study, while other distributions (as Multivariate Poisson) admit only positive correlation. Estimation methods and test of hypothese on the parameters in bivariate case are discussed. The proposed techniques are illustrated by numerical examples, considering counts of two types of defects in 100g of textile fibers produced by four machines, two from one manufacturer and the other two from another one. The results from different regression models are compared. The empirical distribution of Poisson Log-Normal parameter estimations are studied by simulated samples.