This thesis suggests a general approach for estimating the trend of a univariate time series. It begins by suggesting and defining a set of "desirable" trend properties, namely "Fidelity", "Smoothness", "Invariance" and "Additivity", which are then incorporated into the design of an appropriate non-stationary time series model. The unknown parameters of the model are then estimated using a wide selection of "optimal" procedures, each parameter having at least two such procedures applied to it. Attention is paid to the development of algorithms to implement the procedures in practice. The model is gradually extended from a basic, non-seasonal model consisting of a simple lagged trend to a general, seasonal model incorporating a variable parameter, general autoregressive trend.

Diverse probabilistic results are used in the design of random univariate generators. General methods based on these are classified and relevant theoretical properties derived. This is followed by a comparative review of specific algorithms currently available for continuous and discrete univariate distributions. A need for a Zeta generator is established, and two new methods, based on inversion and rejection with a truncated Pareto envelope respectively are developed and compared. The paucity of algorithms for multivariate generation motivates a classification of general methods, and in particular, a new method involving envelope rejection with a novel target distribution is proposed. A new method for generating first passage times in a Wiener Process is constructed. This is based on the ratio of two random numbers, and its performance is compared to an existing method for generating inverse Gaussian variates. New "hybrid" algorithms for Poisson and Negative Binomial distributions are constructed, using an Alias implementation, together with a Geometric tail procedure. These are shown to be robust, exact and fast for a wide range of parameter values. Significant modifications are made to Atkinson's Poisson generator (PA), and the resulting algorithm shown to be complementary to the hybrid method. A new method for Von Mises generation via a comparison of random numbers follows, and its performance compared to that of Best and Fisher's Wrapped Cauchy rejection method. Finally new methods are proposed for sampling from distribution tails, using optimally designed Exponential envelopes. Timings are given for Gamma and Normal tails, and in the latter case the performance is shown to be significantly better than Marsaglia's tail generation procedure.

This thesis focuses on developing a nondestructive strategy for measuring the quality of food using hyperspectral imaging. The specific focus is to develop a classification methodology for detecting bruised/unbruised areas in hyperspectral images of fruits such as strawberries through the classification of pixels containing the edible portion of the fruit. A multiband segmentation algorithm is formulated to generate a mask for extracting the edible pixels from each band in a hypercube. A key feature of the segmentation algorithm is that it makes no prior assumptions for selecting the bands involved in the segmentation. Consequently, different bands may be selected for different hypercubes to accommodate the intra-hypercube variations. Gaussian univariate classifiers are implemented to classify the bruised-unbruised pixels in each band and it is shown that many band classifiers yield 100% classification accuracies. Furthermore, it is shown that the bands that contain the most useful discriminatory information for classifying bruised-unbruised pixels can be identified from the classification results. The strategy developed in this study will facilitate the design of fruit sorting systems using NIR cameras with selected bands.

Industrial processes generate large quantities of process and product quality data. Most of this data is stored and is analyzed in a univariate fashion. However important information may be lost through the implementation of univariate analysis methods. This information is contained in the correlation structure amongst the process and product quality variables and between these two types of variables. Through multivariate analysis this information is retained. As a result process evaluation, prediction and monitoring are more effectively performed. Multivariate data analysis techniques were therefore applied to data sets that summarized three of INCO's Copper Cliff smelting and refining processes. In the first instance the analysis of historical data pertaining to a batch leaching process was undertaken and the time required for leaching was predicted. In the second a multivariate soft sensor was developed in order to predict the concentration of nitric oxide contained in the feed gas to the smelter acid plant. The final project involved the analysis and monitoring of a continuous nickel carbonyl process. The resulting models were evaluated and significant variables with respect to the variation in the process and product quality data and the correlation between them were identified. The product quality data was also well predicted using new process data only that was input to the models. Finally new data was input to the models and the process was monitored using a reduced number of latent variables. Contribution plots were used to identify the original variables that contributed most to the observations that exceeded the established control limits. / Thesis / Master of Science (MSc)

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The cosmopolitan Pteridium aquilinum (L.) Kuhn is widespread throughout eastern North American, where it is represented primarily by Tryon's (1941) var. latiusculum (Desv.) Underw. and var. pseudocaudatum (Clute) Heller. The taxonomy of Pteridium is controversial. Fourteen isozyme loci and 12 morphological characters were used to assess the taxonomic relationship of these two varieties. Isozyme data indicated a high mean genetic identity (I = 0.976) between eleven bracken populations. Strong patterns of geographic variation for isozyme allele frequencies were also observed. The isozyme results did not separate the two taxa. Numerical analysis of the morphology distinguished the two taxa when the qualitative characters were used alone or in conjunction with some of the quantitative traits. All qualitative characters differed significantly between the two taxa. No perceptible geographic pattern of variation was observed. Morphological distinctiveness was maintained even in those localities where both taxa were present, with few or no intermediates being found. Isozyme evidence suggestive of gene flow between the two varieties was found at Greensboro, NC, where the two morphotypes were easily recognizable. The isozyme evidence strongly indicates conspecificity, while the morphological evidence supports their status at the varietal level. / Master of Science

Many of the skills that are considered noteworthy in our societies are either acquired through training and dedication or granted to those lucky enough to have the right genetics and environment. But one skill that is often overlooked is the ability to keep rhythm. This skill is both remarkable in temporal accuracy and common. And although rhythm-keeping is a common cognitive skill, not much is known about the neural correlates of rhythm-keeping. The aim of the current exploratory study was to investigate the electrophysiological correlates of rhythm-keeping. In this study, participants were asked to perform a task where they were introduced to a rhythm with both auditory and visual cues to correct themselves after. They were then supposed to keep the rhythm for approximately thirty seconds, even though all stimuli indicating the rhythm were removed after six seconds of performing the task. During the task, electrophysiological data were collected using electroencephalography (EEG). The electrophysiological data and task performance were then analysed using mass univariate analysis, but no significant relationship was found between the electrophysiological data and rhythm-keeping performance. Potential issues underlying these non-significant findings, as well as solutions to address these, will be addressed.

One of the most important threats for many firms today, despite their nature of the operation, size and longevity, is insolvency. Existing empirical evidence has shown that in the past two decades, business failures have occurred at a higher rate than any time since the 1930s. Many business failure studies have been conducted over time using financial ratios as inputs and traditional statistical techniques. Some of these studies examined whether cash flow information improves the prediction of business failure. Most recently, researchers have employed discriminant analysis to perform business failure prediction. The recent changes in the world caused by unstable environments where many firms fail more than ever, there is increasing need to predict business failure. To this date, there have been limited previous studies conducted on failure prediction for UK firms. Even in other countries, there has been a small amount of research done in the field of firm failures. Therefore, this study investigates the extension of Altman’s (1968) original model in predicting the health of UK firms using discriminant analysis and performance ratios to test which ratios are statistically significant in predicting the health of the UK firms .a selected sample containing 90 failed and 1000 non failed on UK industrial firms from 2000 – 2013. The main purpose of this study is to contribute towards Altman’s (1968) original Z-score model by adding new variables (Cash flow ratio). The study found that cash flow, when combined with Altman’s original variables is highly significant in predicting the health of UK general firms. A J-UK model was developed to test the health of UK firms. When compared with the re-estimated the Altman’s original model in the UK context, the predictive power of the model was 82.9%, which is consistent with Taffler’s (1982) UK model. Furthermore, to test the predictive power of the model before, during and after the financial crisis periods; results show that J-UK model had a higher accuracy to predict the health of UK firms than the re-estimated Altman’s original model. Finally, the study proves that liquidity, profitability, leverage and capital turnover ratios are significant ratios in predicting failure. Liquidity and profitability have the highest contribution to the results of both re-estimated Altman’s original model and J-UK model. This study has implications for decision makers. Regulatory bodies and practitioners have to take into account the ratios, which contributed highest to the model in order to serve as early warning signals for corrective action.

O presente estudo tem como objetivo identificar os papéis na compra de interruptores de luz por clientes finais e identificar as influências desses papéis e como ocorrem. O método utilizado foi dividido em duas fases, a primeira com abordagem qualitativa e a segunda com abordagem quantitativa. Inicialmente foram feitas entrevistas em profundidade com consumidores e profissionais da área, com o objetivo de identificar quais os tipos de papéis que ocorrem durante a compra de interruptores e identificar os influenciadores. O segundo passo foi uma survey, sendo entrevistadas 1.013 pessoas divididas nas cinco regiões do país, Região Centro Oeste, Nordeste, Sul, Norte e Sudeste. Foram identificados cinco papéis de compra e sete influenciadores desse processo. Os resultados da pesquisa quantitativa foram analisados com análises univariadas e multivariadas. As análises indicam que os compradores adquirem interruptores para outros usuários, mas a grande maioria deles adquirem o produto para uso próprio e são responsáveis pela compra e pelo pagamento do produto. Os resultados também indicam que as pessoas próximas são os maiores influenciadores. Espera-se que com os resultados obtidos esse trabalho possa contribuir para executivos e empresas do setor elétrico na tomada de decisões. / The present paper has the objective to identify which are the final consumers purchase roles of light switches and identify the influences about this roles and how it occurs. The analysis method was dividing in two steps, the first with a qualitative approach and the second with a quantitative approach. First of all, had been made depth interviews with consumers and professionals from the area, with the objective to identify the influencers. After that, a survey has been made, 1.013 people were interviewed and divided at five regions of the country, Midwest region, Northeast, South, North and Southeast. Were identified five purchase roles and seven influencers of this process. The quantitative research results were analyzed with univariate and multivariate analysis. The analysis indicate that the buyers also buy the light switches for other users, but the majority buy the product for own use and is responsible for the purchase and the product payment. The results also indicate that, in general, close people are the major influencers. With the obtained results throw crossing and the performed analyses, the expectations with this paper is contribute with executives and companies of the area on theirs decision-making.

Functional brain images are extraordinarily rich data sets that reveal distributed brain networks engaged in a wide variety of cognitive operations. It is a substantial challenge both to create models of cognition that mimic behavior and underlying cognitive processes and to choose a suitable analytic method to identify underlying brain networks. Most of the contemporary techniques used in analyses of functional neuroimaging data are based on univariate approaches in which single image elements (i.e. voxels) are considered to be computationally independent measures. Beyond univariate methods (e.g. statistical parametric mapping), multivariate approaches, which identify a network across all regions of the brain rather than a tessellation of regions, are potentially well suited for analyses of brain imaging data. A multivariate method (e.g. partial least squares) is a computational strategy that determines time-varying distributed patterns of the brain (as a function of a cognitive task). Compared to its univariate counterparts, a multivariate approach provides greater levels of sensitivity and reflects cooperative interactions among brain regions. Thus, by considering information across more than one measuring point, additional information on brain function can be revealed. Similarly, by considering information across more than one measuring technique, the nature of underlying cognitive processes become well-understood. Cognitive processes have been investigated in conjunction with multiple neuroimaging modalities (e.g. fMRI, sMRI, EEG, DTI), whereas the typical method has been to analyze each modality separately. Accordingly, little work has been carried out to examine the relation between different modalities. Indeed, due to the interconnected nature of brain processing, it is plausible that changes in one modality locally or distally modulate changes in another modality. This thesis focuses on multivariate and multimodal methods of image analysis applied to various cognitive questions. These methods are used in order to extract features that are inaccessible using univariate / unimodal analytic approaches. To this end, I implemented multivariate partial least squares analysis in study I and II in order to identify neural commonalities and differences between the available and accessible information in memory (study I), and also between episodic encoding and episodic retrieval (study II). Study I provided evidence of a qualitative differences between availability and accessibility signals in memory by linking memory access to modality-independent brain regions, and availability in memory to elevated activity in modality-specific brain regions. Study II provided evidence in support of general and specific memory operations during encoding and retrieval by linking general processes to the joint demands on attentional, executive, and strategic processing, and a process-specific network to core episodic memory function. In study II, III, and IV, I explored whether the age-related changes/differences in one modality were driven by age-related changes/differences in another modality. To this end, study II investigated whether age-related functional differences in hippocampus during an episodic memory task could be accounted for by age-related structural differences. I found that age-related local structural deterioration could partially but not entirely account for age-related diminished hippocampal activation. In study III, I sought to explore whether age-related changes in the prefrontal and occipital cortex during a semantic memory task were driven by local and/or distal gray matter loss. I found that age-related diminished prefrontal activation was driven, at least in part, by local gray matter atrophy, whereas the age-related decline in occipital cortex was accounted for by distal gray matter atrophy. Finally, in study IV, I investigated whether white matter (WM) microstructural differences mediated age-related decline in different cognitive domains. The findings implicated WM as one source of age-related decline on tasks measuring processing speed, but they did not support the view that age-related differences in episodic memory, visuospatial ability, or fluency were strongly driven by age-related differences in white-matter pathways. Taken together, the architecture of different aspects of episodic memory (e.g. encoding vs. retrieval; availability vs. accessibility) was characterized using a multivariate partial least squares. This finding highlights usefulness of multivariate techniques in guiding cognitive theories of episodic memory. Additionally, competing theories of cognitive aging were investigated by multimodal integration of age-related changes in brain structure, function, and behavior. The structure-function relationships were specific to brain regions and cognitive domains. Finally, we urged that contemporary theories on cognitive aging need to be extended to longitudinal measures to be further validated.