Thesis (MComm)-- Stellenbosch University, 2014. / ENGLISH ABSTRACT: Extreme value theory (EVT) involves the development of statistical models and techniques in order to describe and model extreme events. In order to make inferences about extreme quantiles, it is necessary to estimate the extreme value index (EVI). Numerous estimators of the EVI exist in the literature. However, these estimators are only applicable in the single sample setting. The aim of this study is to obtain an improved estimator of the EVI that is applicable to an ANOVA setting. An ANOVA setting lends itself naturally to empirical Bayes (EB) estimators, which are the main estimators under consideration in this study. EB estimators have not received much attention in the literature. The study begins with a literature study, covering the areas of application of EVT, Bayesian theory and EB theory. Different estimation methods of the EVI are discussed, focusing also on possible methods of determining the optimal threshold. Specifically, two adaptive methods of threshold selection are considered. A simulation study is carried out to compare the performance of different estimation methods, applied only in the single sample setting. First order and second order estimation methods are considered. In the case of second order estimation, possible methods of estimating the second order parameter are also explored. With regards to obtaining an estimator that is applicable to an ANOVA setting, a first order EB estimator and a second order EB estimator of the EVI are derived. A case study of five insurance claims portfolios is used to examine whether the two EB estimators improve the accuracy of estimating the EVI, when compared to viewing the portfolios in isolation. The results showed that the first order EB estimator performed better than the Hill estimator. However, the second order EB estimator did not perform better than the “benchmark” second order estimator, namely fitting the perturbed Pareto distribution to all observations above a pre-determined threshold by means of maximum likelihood estimation. / AFRIKAANSE OPSOMMING: Ekstreemwaardeteorie (EWT) behels die ontwikkeling van statistiese modelle en tegnieke wat gebruik word om ekstreme gebeurtenisse te beskryf en te modelleer. Ten einde inferensies aangaande ekstreem kwantiele te maak, is dit nodig om die ekstreem waarde indeks (EWI) te beraam. Daar bestaan talle beramers van die EWI in die literatuur. Hierdie beramers is egter slegs van toepassing in die enkele steekproef geval. Die doel van hierdie studie is om ’n meer akkurate beramer van die EWI te verkry wat van toepassing is in ’n ANOVA opset. ’n ANOVA opset leen homself tot die gebruik van empiriese Bayes (EB) beramers, wat die fokus van hierdie studie sal wees. Hierdie beramers is nog nie in literatuur ondersoek nie. Die studie begin met ’n literatuurstudie, wat die areas van toepassing vir EWT, Bayes teorie en EB teorie insluit. Verskillende metodes van EWI beraming word bespreek, insluitend ’n bespreking oor hoe die optimale drempel bepaal kan word. Spesifiek word twee aanpasbare metodes van drempelseleksie beskou. ’n Simulasiestudie is uitgevoer om die akkuraatheid van beraming van verskillende beramingsmetodes te vergelyk, in die enkele steekproef geval. Eerste orde en tweede orde beramingsmetodes word beskou. In die geval van tweede orde beraming, word moontlike beramingsmetodes van die tweede orde parameter ook ondersoek. ’n Eerste orde en ’n tweede orde EB beramer van die EWI is afgelei met die doel om ’n beramer te kry wat van toepassing is vir die ANAVA opset. ’n Gevallestudie van vyf versekeringsportefeuljes word gebruik om ondersoek in te stel of die twee EB beramers die akkuraatheid van beraming van die EWI verbeter, in vergelyking met die EWI beramers wat verkry word deur die portefeuljes afsonderlik te ontleed. Die resultate toon dat die eerste orde EB beramer beter gevaar het as die Hill beramer. Die tweede orde EB beramer het egter slegter gevaar as die tweede orde beramer wat gebruik is as maatstaf, naamlik die passing van die gesteurde Pareto verdeling (PPD) aan alle waarnemings bo ’n gegewe drempel, met behulp van maksimum aanneemlikheidsberaming.

Difficulties in speech production are often marked by disfluency; fillers, hesitations, prolongations, repetitions and repairs. In recent years a body of work has emerged that demonstrates that listeners are sensitive to disfluency, and that this affects their expectations for upcoming speech, as well as their attention to the speech stream. This thesis investigates the extent to which delay may be responsible for triggering these effects. The experiments reported in this thesis build on an Event Related Potential (ERP) paradigm developed by Corley et al., (2007), in which participants listened to sentences manipulated by both fluency and predictability. Corley et al. reported an attenuated N400 effect for words following disfluent ers, and interpreted this as indicating that the extent to which listeners made predictions was reduced following an er. In the current set of experiments, various noisy interruptions were added to Corley et al.,'s paradigm, time matched to the disfluent fillers. These manipulations allowed investigation of whether the same effects could be triggered by delay alone, in the absence of a cue indicating that the speaker was experiencing difficulty. The first experiment, which contrasted disfluent ers with artificial beeps, revealed a small but significant reduction in N400 effect amplitude for words affected by ers but not by beeps. The second experiment, in which ers were contrasted with speaker generated coughs, revealed no fluency effects on the N400 effect. A third experiment combined the designs of Experiments 1 and 2 to verify whether the difference between them could be characterised as a context effect; one potential explanation for the difference between the outcomes of Experiments 1 and 2 is that the interpretation of an er is affected by the surrounding stimuli. However, in Experiment 3, once again no effect of fluency on the magnitude of the N400 effect was found. Taken together, the results of these three studies lead to the question of whether er's attenuation effect on the N400 is robust. In a second part to each study, listeners took part in a surprise recognition memory test, comprising words which had been the critical words in the previous task intermixed with new words which had not appeared anywhere in the sentences previously heard. Participants were significantly more successful at recognising words which had been unpredictable in their contexts, and, importantly, for Experiments 1 and 2, were significantly more successful at recognising words which had featured in disfluent or interrupted sentences. There was no difference between the recognition rates of words which had been disfluent and those which were affected by a noisy interruption. Collard et al., (2008) demonstrated that disfluency could raise attention to the speech stream, and the finding that interrupted words are equally well remembered leads to the suggestion that any noisy interruption can raise attention. Overall, the finding of memory benefits in response to disfluency, in the absence of attenuated N400 effects leads to the suggestion that different elements of disfluencies may be responsible for triggering these effects. The studies in this thesis also extend previous work by being designed to yield enough trials in the memory test portion of each experiment to permit ERP analysis of the memory data. Whilst clear ERP memory effects remained elusive, important progress was made in that memory ERPs were generated from a disfluency paradigm, and this provided a testing ground on which to demonstrate the use of linear mixed-effects modelling as an alternative to ANOVA analysis for ERPs. Mixed-effects models allow the analysis of unbalanced datasets, such as those generated in many memory experiments. Additionally, we demonstrate the ability to include crossed random effects for subjects and items, and when this is applied to the ERPs from the listening section of Experiment 1, the effect of fluency on N400 amplitude is no longer significant. Taken together, the results from the studies reported in this thesis suggest that temporal delay or disruption in speech can trigger raised attention, but do not necessarily trigger changes in listeners' expectations.

The study seeks to determine the effect upon the F-statistic of violating the assumption of homogeneity of regression slopes in the one-way, fixed-effects analysis of covariance model. The study employs a Monte Carlo simulation technique to vary the degree of heterogeneity of regression slopes with varied sample sizes within experiments to determine the effect of such conditions. One hundred and eighty-three simulations were used.

For years attribution research has been dominated by the ANOVA model of behavior which proposes that people construct their dispositional attributions of others by carefully comparing and weighing all situational information using mental computations similar to the processes used by researchers to analyze data. A preliminary experiment successfully determined that participants were able to distinguish differences in variability assessed across persons (high vs. low consensus) and across situations (high vs. low distinctiveness). Also, it was clear that the subjects could evaluate varying levels of situational constraint. A primary experiment administered to participants immediately following the preliminary study determined that participants grossly under-utilized those same variables when making dispositional attributions. Results gave evidence against the use of traditional ANOVA models and support for the use of the Behavior Averaging Principle of Attribution. / by Kori A. Hakala. / Thesis (M.A.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Métodos não-paramétricos têm aplicação ampla na análise de dados, tendo em vista que não são limitados pela necessidade de imposição de distribuições populacionais específicas. O caráter multivariado de dados provenientes de estudos nas ciências do comportamento, ecológicos, experimentos agrícolas e muitos outros tipos, e o crescimento contínuo da tecnologia computacional, têm levado a um crescente interesse no uso de métodos multivariados não-paramétricos. A aplicação da análise de variância multivariada não-paramétrica é pouco inacessível ao pesquisador, exceto através de métodos aproximados baseados nos valores assintóticos da estatística de teste. Portanto, este trabalho tem por objetivo apresentar uma rotina na linguagem C que realiza testes baseados numa extensão multivariada do teste univariado de Kruskal- Wallis, usando a técnica das permutações. Para pequenas amostras, todas as configurações de tratamentos são obtidas para o cálculo do valor-p. Para grandes amostras, um número fixo de configurações aleatórias é usado, obtendo assim valores de significância aproximados. Além disso, um teste alternativo é apresentado com o uso de componentes principais baseados nas matrizes de postos. / Nonparametric methods have especially broad applications in the analysis of data since they are not bound by restrictions on the population distribution. The multivariate character of behavioural, ecological, agricultural and many other types of data and the continued improvement in computer technology have led to a sharp interest in the use of nonparametric multivariate methods in data analysis. The application of nonparametric multivariate analysis is inaccessible to applied research, except by approximation methods based on asymptotic values of the test statistic. Thus, this work aims to presenting a routine in the C language that runs multivariate tests based on a multivariate extension of the univariate Kruskal-Wallis test, using permutation technique. For small samples, all possible treatment configurations are used in order to obtain the p-value. For large samples, a fixed number of random configurations are used, obtaining an approximated significance values. In addition, another alternative test is presented using principal components based on rank matrices.

This study sought to determine the effect upon Tukey's Honestly Significant Difference (HSD) statistic of concurrently violating the assumptions of homogeneity of variance and equal sample sizes. Two forms for the unequal sample size problem were investigated. Kramer's form and the harmonic mean approach were the two unequal sample size procedures studied. The study employed a Monte Carlo simulation procedure which varied sample sizes with a heterogeneity of variance condition. Four thousand experiments were generated. Findings of this study were based upon the empirically obtained significance levels. Five conclusions were reached in this study. The first conclusion was that for the conditions of this study the Kramer form of the HSD statistic is not robust at the .05 or .01 nominal level of significance. A second conclusion was that the harmonic mean form of the HSD statistic is not robust at the .05 and .01 nominal level of significance. A general conclusion reached from all the findings formed the third conclusion. It was that the Kramer form of the HSD test is the preferred procedure under combined assumption violations of variance heterogeneity and unequal sample sizes. Two additional conclusions are based on related findings. The fourth conclusion was that for the combined assumption violations in this study, the actual significance levels (probability levels) were less-than the nominal significance levels when the magnitude of the unequal variances were positively related to the magnitude of the unequal sample sizes. The fifth and last conclusion was that for the concurrent assumption violation of variance heterogeneity and unequal sample sizes, the actual significance levels significantly exceed the nominal significance levels when the magnitude of the unequal variances are negatively related to the magnitude of the unequal sample sizes.

This thesis presents a contribution to the active research area of functional data analysis (FDA) and is concerned with the analysis of data from complex experimental designs in which the responses are curves. High resolution, closely correlated data sets are encountered in many research fields, but current statistical methodologies often analyse simplistic summary measures and therefore limit the completeness and accuracy of conclusions drawn. Specifically the nature of the curves and experimental design are not taken into account. Mathematically, such curves can be modelled either as sample paths of a stochastic process or as random elements in a Hilbert space. Despite this more complex type of response, the structure of experiments which yield functional data is often the same as in classical experimentation. Thus, classical experimental design principles and results can be adapted to the FDA setting. More specifically, we are interested in the functional analysis of variance (ANOVA) of experiments which use orthogonal designs. Most of the existing functional ANOVA approaches consider only completely randomised designs. However, we are interested in more complex experimental arrangements such as, for example, split-plot and row-column designs. Similar to univariate responses, such complex designs imply that the response curves for different observational units are correlated. We use the design to derive a functional mixed-effects model and adapt the classical projection approach in order to derive the functional ANOVA. As a main result, we derive new functional F tests for hypotheses about treatment effects in the appropriate strata of the design. The approximate null distribution of these tests is derived by applying the Karhunen- Lo`eve expansion to the covariance functions in the relevant strata. These results extend existing work on functional F tests for completely randomised designs. The methodology developed in the thesis has wide applicability. In particular, we consider novel applications of functional F tests to gait analysis. Results are presented for two empirical studies. In the first study, gait data of patients with cerebral palsy were collected during barefoot walking and walking with ankle-foot orthoses. The effects of ankle-foot orthoses are assessed by functional F tests and compared with pointwise F tests and the traditional univariate repeated-measurements ANOVA. The second study is a designed experiment in which a split-plot design was used to collect gait data from healthy subjects. This is commonly done in gait research in order to better understand, for example, the effects of orthoses while avoiding confounded analysis from the high variability observed in abnormal gait. Moreover, from a technical point of view the study may be regarded as a real-world alternative to simulation studies. By using healthy individuals it is possible to collect data which are in better agreement with the underlying model assumptions. The penultimate chapter of the thesis presents a qualitative study with clinical experts to investigate the utility of gait analysis for the management of cerebral palsy. We explore potential pathways by which the statistical analyses in the thesis might influence patient outcomes. The thesis has six chapters. After describing motivation and introduction in Chapter 1, mathematical representations of functional data are presented in Chapter 2. Chapter 3 considers orthogonal designs in the context of functional data analysis. New functional F tests for complex designs are derived in Chapter 4 and applied in two gait studies. Chapter 5 is devoted to a qualitative study. The thesis concludes with a discussion which details the extent to which the research question has been addressed, the limitations of the work and the degree to which it has been answered.

Orientador: Vicente Lopes Junior / Banca: Samuel Silva / Banca: Michael John Brennan / Banca: Carlos Alberto Cimini Junior / Resumo: Pesquisadores de diversas partes do mundo se empenham em desenvolver técnicas capazes de monitorar a integridade de máquinas, veículos e estruturas, principalmente as que a ruptura ou destruição possa provocar acidentes e catástrofes. Neste contexto, várias técnicas não destrutivas podem ser utilizadas para monitorar estes sistemas permitindo a realização de reparos e, evitando maiores prejuízos econômicos e danos sociais. A técnica da Impedância Eletromecânica está entre as técnicas baseadas na utilização de materiais piezelétricos e, particularmente, utiliza-se de uma curva sensível a pequenas variações na estrutura, característica que faz a técnica ser eficiente na detecção de danos incipientes. No entanto, sob variações das condições ambiente e de teste, a sensibilidade da técnica pode produzir falsos diagnósticos. Desta forma, o desafio atual é aplicar a técnica da Impedância Eletromecânica em sistemas de monitoramento considerando condições mais próximas às condições de operação reais dos sistemas a serem monitorados. Este trabalho apresenta duas metodologias para sistemas SHM, a primeira consiste em utilizar a técnica de agrupamento Fuzzy c-means para entender e considerar o efeito da temperatura nos sinais da Impedância Eletromecânica. A segunda metodologia utiliza análise de variância (ANOVA) para propor uma metodologia de detecção mais robusta, e assim, incorporar variações aleatórias nos sistemas de medição e aquisição sem comprometer o diagnóstico SHM / Abstract: Researchers around the world are engaged to develop techniques for structural health monitoring of machinery, vehicles and structures, especially systems where damage or destruction could induce accidents and disasters. In this context, several non-destructive techniques can be used to monitor these systems allowing repairs and avoiding major economic losses or social losses. The electromechanical impedance technique is among the techniques based on piezoelectric materials use and it is sensible to small variations in the structure which makes it efficient in detecting incipient damages. However, variations in the ambient or test conditions can cause false diagnoses. Therefore, the current challenge is to apply the electromechanical impedance technique considering monitoring conditions closer to real operating conditions of the systems to be monitored. This work presents two methodologies for SHM systems. The first one uses Fuzzy c-means clustering to distinguish the temperature effect on impedance signal. The second method uses analysis of variance (ANOVA) to propose a more robust detection methodology and thus incorporate random variations in measurement systems and acquisition without loss of SHM diagnostic / Mestre

Philosophiae Doctor - PhD (Industrial Psychology) / Organizations exist for the primary aim of meeting particular objectives: innovation and advancement, customer satisfaction, profit making and delivery of quality goods and services. These goals are mostly channelled with the intent of demonstrating high performance crucial for the continued existence of the organization especially in these rapidly changing global economies. This target, however, cannot be achieved without the aid of employees in the organization. A plethora of previous studies have proven that efficiency, productivity, high performance and stability on the job can be better achieved when the employees are dedicated, committed to their work roles and experience work engagement. The experience of work engagement on the part of the employees is not a random event; it depends on a myriad of factors that include authentic leadership. Leaders have a cumulative change effect on their followers; hence, leaders in an organization can be termed as core drivers of employee engagement. Hence, it becomes imperative to seek to understand what authentic leadership as a construct has to offer to the workplace.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Bahman Daryanian. / M.S.