This study investigated the impact of three edaphic factors on the distribution of forest plants in Sweden. Based on 2657 plots with 22 common species, Canonical Correspondence Analysis (CCA) and Generalized-linear-model (GLM) were performed with pH measurements in the top layer of the soil, latitude and deciduous tree proportion as explanatory variables. Variation of the species occurrence could to a substantial degree be explained by pH, latitude and proportion of timber volume of deciduous tree species. Furthermore, the majority of species were affected by the studied environmental variables. Therefore, these factors have an important role in the ecological interactions in the forest. All species also showed broad pH-niches with many occurrences spread out within the species entire pH-range. Finally, the study relates to educational science through designing a meaningful excursion for secondary school when teaching ecology.

Questões centrais em diversas áreas do conhecimento como ciências ambientais, geologia, agronomia, dentre outras, envolvem a compreensão da distribuição espacial de processos a partir de dados espacialmente referenciados. Os interesses de pesquisa podem estar na descrição espacial de duas ou mais variáveis e, desta forma, tem-se dois ou mais atributos para modelar. Modelos multivariados são propostos para o estudo se há evidências e/ou explicações contextuais de que os processos não são independentes. Diferentes modelos propostos na literatura foram avaliados e comparados ao modelo Matérn multivariado, recentemente proposto na literatura. Foram considerados o modelo linear de corregionalização, o modelo bivariado gaussiano de componente comum e um modelo bayesiano de regressão espacial. Estes modelos foram ajustados e utilizados para predição espacial geoestatística (krigagem) em um conjunto de dados com duas variáveis climáticas no qual uma parte dos dados foi separada para avaliação das predições. Além disso, foi realizado um estudo de simulação para avaliar a estimação e predição sob o modelo Matérn multivariado. / Key issues in a diversity of subject areas such as environmental sciences, geology, agronomy, among other, require the understanding of the spatial distribution of natural processes from spatially referenced data. Research interests may include the spatial description of two or more variables and therefore, there are tow or more attributes to be modeled. Multivariate models are adopted when there is evidence and/or contextual explanations the two processes are not independent. Different models presented in the literature are assessed and compared to the recently introduced multivariate Matérn model. The linear model of corregionalization, the bivariate Gaussian common component model and a bayesian spatial reression model were considered. The models were fitted and used for geostatistical spatial prediction (kriging) for a pair of weather related variables with part of the data used only for comparing the predicions. Additionally a simulation study assessed estimation and prediction under the multivariate Matérn model.

Dados binários ou dicotômicos são comuns em muitas áreas das ciências, nas quais, muitas vezes, há interesse em registrar a ocorrência, ou não, de um evento particular. Por outro lado, quando cada unidade amostral é avaliada em mais de uma ocasião no tempo, tem-se dados longitudinais ou medidas repetidas no tempo. é comum também, nesses estudos, se ter uma ou mais variáveis explicativas associadas às variáveis respostas. As variáveis explicativas podem ser dependentes ou independentes do tempo. Na literatura, há técnicas disponíveis para a modelagem e análise desses dados, sendo os modelos disponíveis extensões dos modelos lineares generalizados. O enfoque do presente trabalho é dado aos modelos lineares generalizados de transição para a análise de dados longitudinais envolvendo uma resposta do tipo binária. Esses modelos são baseados em processos estocásticos e o interesse está em modelar as probabilidades de mudanças ou transições de categorias de respostas dos indivíduos no tempo. A suposição mais utilizada nesses processos é a da propriedade markoviana, a qual condiciona a resposta numa dada ocasião ao estado na ocasião anterior. Assim, são revistos os fundamentos para se especificar tais modelos, distinguindo-se os casos estacionário e não-estacionário. O método da máxima verossimilhança é utilizado para o ajuste dos modelos e estimação das probabilidades. Adicionalmente, apresentam-se testes assintóticos para comparar tratamentos, baseados na razão de chances e na diferença das probabilidades de transição. Outra questão explorada é a combinação do modelo de efeitos aleatórios com a do modelo de transição. Os métodos são ilustrados com um exemplo da área da saúde. Para esses dados, o processo é considerado estacionário de ordem dois e o teste proposto sinaliza diferença estatisticamente significativa a favor do tratamento ativo. Apesar de ser uma abordagem inicial dessa metodologia, verifica-se, que os modelos de transição têm notável aplicabilidade e são fontes para estudos e pesquisas futuras. / Binary or dichotomous data are quite common in many fields of Sciences in which there is an interest in registering the occurrence of a particular event. On the other hand, when each sampled unit is evaluated in more than one occasion, we have longitudinal data or repeated measures over time. It is also common, in longitudinal studies, to have explanatory variables associated to response measures, which can be time dependent or independent. In the literature, there are many approaches to modeling and evaluating these data, where the models are extensions of generalized linear models. This work focus on generalized linear transition models suitable for analyzing longitudinal data with binary response. Such models are based on stochastic processes and we aim to model the probabilities of change or transitions of individual response categories in time. The most used assumption in these processes is the Markov property, in which the response in one occasion depends on the immediately preceding response. Thus we review the fundamentals to specify these models, showing the diferences between stationary and non-stationary processes. The maximum likelihood approach is used in order to fit the models and estimate the probabilities. Furthermore, we show asymptotic tests to compare treatments based on odds ratio and on the diferences of transition probabilities. We also present a combination of random-efects model with transition model. The methods are illustrated with health data. For these data, the process is stationary of order two and the suggested test points to a significant statistical diference in favor of the active treatment. This work is an initial approach to transition models, which have high applicability and are great sources for further studies and researches.

La modélisation des systèmes dynamiques requiert la prise en compte d’incertitudes liées à l’existence inévitable de bruits (bruits de mesure, bruits sur la dynamique), à la méconnaissance de certains phénomènes perturbateurs mais également aux incertitudes sur la valeur des paramètres (spécification de tolérances, phénomène de vieillissement). Alors que certaines de ces incertitudes se prêtent bien à une modélisation de type statistique comme par exemple ! les bruits de mesure, d’autres se caractérisent mieux pa ! r des bornes, sans autre attribut. Dans ce travail de thèse, motivés par les observations ci-dessus, nous traitons le problème de l’intégration d’incertitudes statistiques et à erreurs bornées pour les systèmes linéaires à temps discret. Partant du filtre de Kalman Intervalle (noté IKF) développé dans [Chen 1997], nous proposons des améliorations significatives basées sur des techniques récentes de propagation de contraintes et d’inversion ensembliste qui, contrairement aux mécanismes mis en jeu par l’IKF, permettent d’obtenir un résultat garanti tout en contrôlant le pessimisme de l’analyse par intervalles. Cet algorithme est noté iIKF. Le filtre iIKF a la même structure récursive que le filtre de Kalman classique et délivre un encadrement de tous les estimés optimaux et des matrices de covariance possibles. L’algorithme IKF précédent évite quant à lui le problème de l’inversion des matrices intervalles, ce qui lui vaut de perdre des solutions possibles. Pour l’iIKF, nous proposons une méthode originale garantie pour l’inversion des matrices intervalle qui couple l’algorithme SIVIA (Set Inversion via Interval Analysis) et un ensemble de problèmes de propagation de contraintes. Par ailleurs, plusieurs mécanismes basés sur la propagation de contraintes sont également mis en œuvre pour limiter l’effet de surestimation due à la propagation d’intervalles dans la structure récursive du filtre. Un algorithme de détection de défauts basé sur iIKF est proposé en mettant en œuvre une stratégie de boucle semi-fermée qui permet de ne pas réalimenter le filtre avec des mesures corrompues par le défaut dès que celui-ci est détecté. A travers différents exemples, les avantages du filtre iIKF sont exposés et l’efficacité de l’algorithme de détection de défauts est démontré. / In this thesis, a new approach to estimation problems under the presence of bounded uncertain parameters and statistical noise has been presented. The objective is to use the uncertainty model which appears as the most appropriate for every kind of uncertainty. This leads to the need to consider uncertain stochastic systems and to study how the two types of uncertainty combine : statistical noise is modeled as the centered gaussian variable and the unknown but bounded parameters are approximated by intervals. This results in an estimation problem that demands the development of mixed filters and a set-theoretic strategy. The attention is drawn on set inversion problems and constraint satisfaction problems. The former is the foundation of a method for solving interval equations, and the latter can significantly improve the speed of interval based arithmetic and algorithms. An important contribution of this work consists in proposing an interval matrix inversion method which couples the algorithm SIVIA with the construction of a list of constraint propagation problems. The system model is formalized as an uncertain stochastic system. Starting with the interval Kalman filtering algorithm proposed in [Chen 1997] and that we name the IKF, an improved interval Kalman filtering algorithm (iIKF) is proposed. This algorithm is based on interval conditional expectation for interval linear systems. The iIKF has the same structure as the conventional Kalman filter while achieving guaranteed statistical optimality. The recursive computational scheme is developed in the set-membership context. Our improvements achieve guaranteed interval inversion whereas the original version IKF [Chen 1997] uses an instance (the upper bound) of the interval matrix to avoid the possible singularity problems. This point of view leads to a sub-optimal solution that does not preserve guaranteed results, some solutions being lost. On the contrary, in the presence of unknown-but-bounded parameters and measurement statistical errors, our estimation approach in the form of the iIKF provides guaranteed estimates, while maintaining a computational burden comparable to that of classic statistical approaches. Several constraint based techniques have also been implemented to limit the overestimation effect due to interval propagation within the interval Kalman filter recursive structure. The results have shown that the iIKF out puts bounded estimates that enclose all the solutions consistent with bounded errors and achieves good overestimation control. iIKF is used to propose a fault detection algorithm which makes use of a Semi-Closed Loop strategy which does not correct the state estimate with the measure as soon as a fault is detected. Two methods for generating fault indicators are proposed : they use the a priori state estimate and a threshold based on the a posteriori and a priori covariance matrix, respectively, and check the consistency against the measured output. Through different examples, the advantages of the iIKF with respect to previous versions are exhibited and the efficiency of the iIKF based Semi-Closed Loop fault detection algorithm is clearly demonstrated.

A regressão logística com resposta contínua é uma alternativa à regressão logística usual quando a variável resposta possui distribuição contínua e o objetivo do estudo é estimar a probabilidade de ocorrência de valores acima ou abaixo de um determinado valor de corte. O modelo assim construído pode ser escrito na forma de um modelo linear generalizado com função de ligação composta. Quando corretamente especificada, a incorporação da informação sobre a distribuição da variável resposta no modelo faz com que os estimadores de máxima verossimilhança sejam mais eficientes. A técnica é apresentada para os casos em que a variável resposta tem distribuição normal ou log-normal. Como aplicação, considerando dados referentes à cidade de São Paulo nos anos de 1998 e 1999, um modelo de regressão logística com resposta contínua foi considerado na previsão do risco da concentração do poluente NO2 ser maior que um valor de corte estabelecido por legislação. Variáveis climáticas e temporais foram consideradas como preditoras. Mostraram-se importantes para prever o risco a temperatura, a umidade relativa do ar, os dias da semana, as estações do ano, precipitação pluviométrica e velocidade do vento. / Binary regression with continuous outcomes constitutes an alternative to logistic regression when the outcome is continuous and the investigator’s interest focuses to estimate the probability of subjects who fall above or below a cut-off value. The model is based on a generalized linear model with composite link that takes advantage of the continuous structure of the outcome, typically gaussian or lognormal. Under correct response model-ling, binary regression with continuous outcomes is more efficient than logistic regression. A binary regression with continuous outcomes was considered to predict the risk that a NO2 pollutant concentration is above the limits set by environmental legislation in São Paulo city during 1998 and 1999. Climatic and temporal variables were considered as pre-dictors. Temperature, humidity, days of the week, station of the year, precipitation and speed of the wind revealed important to predict the risk.

A regressão logística com resposta contínua é uma alternativa à regressão logística usual quando a variável resposta possui distribuição contínua e o objetivo do estudo é estimar a probabilidade de ocorrência de valores acima ou abaixo de um determinado valor de corte. O modelo assim construído pode ser escrito na forma de um modelo linear generalizado com função de ligação composta. Quando corretamente especificada, a incorporação da informação sobre a distribuição da variável resposta no modelo faz com que os estimadores de máxima verossimilhança sejam mais eficientes. A técnica é apresentada para os casos em que a variável resposta tem distribuição normal ou log-normal. Como aplicação, considerando dados referentes à cidade de São Paulo nos anos de 1998 e 1999, um modelo de regressão logística com resposta contínua foi considerado na previsão do risco da concentração do poluente NO2 ser maior que um valor de corte estabelecido por legislação. Variáveis climáticas e temporais foram consideradas como preditoras. Mostraram-se importantes para prever o risco a temperatura, a umidade relativa do ar, os dias da semana, as estações do ano, precipitação pluviométrica e velocidade do vento. / Binary regression with continuous outcomes constitutes an alternative to logistic regression when the outcome is continuous and the investigator’s interest focuses to estimate the probability of subjects who fall above or below a cut-off value. The model is based on a generalized linear model with composite link that takes advantage of the continuous structure of the outcome, typically gaussian or lognormal. Under correct response model-ling, binary regression with continuous outcomes is more efficient than logistic regression. A binary regression with continuous outcomes was considered to predict the risk that a NO2 pollutant concentration is above the limits set by environmental legislation in São Paulo city during 1998 and 1999. Climatic and temporal variables were considered as pre-dictors. Temperature, humidity, days of the week, station of the year, precipitation and speed of the wind revealed important to predict the risk.

Neste trabalho estudamos as aplicações do método do Gradiente Espectral Projetado (SPG) em Meteorologia nos campos de previsibilidade, estabilidade e sensibilidade. Inicialmente revisamos os Vetores Singulares Lineares (LSVs) e em seguida apresentamos a teoria das Perturbações Condicionais Não-Lineares Ótimas (CNOPs). Enquanto os métodos clássicos estão baseados no Modelo Tangente Linear, as CNOPs são uma formulação do mesmo problema baseado em Programação Não-Linear. As CNOPs são descritas na literatura como responsáveis por melhorias em relação aos métodos anteriores. Finalmente analisamos três exemplos de aplicação do método à problemas de previsibilidade, estabilidade e sensibilidade. / A revision about applications of Spectral Projected Gradient (SPG) in meteorology is done in the fields of predictability, stability and sensitivity. Initially we review about Linear Singular Vectos (LSVs) and we present the Conditional Nonlinear Optimal perturbations (CNOPs). While the classic methods are based on the Tangent Linear Model, CNOPs are another formulation of the problem based on Nonlinear Programming. CNOPs are described in bibliography as responsible by better results than older methods. Finally we analyze three applications in predictability, stability and sensibility.

This two-article dissertation summarizes the end-of-course survey and formative feedback literatures, as well as proposes actionability as a useful construct in the analysis of feedback from students captured in real-time during their courses. The present inquiry grew out of my work as the founder of DropThought Education, a Division of DropThought. DropThought Education was a student feedback system that helped instructional designers, instructors, and educational systems to use feedback from students to improve learning and student experience. To find out whether the DropThought style of feedback was more effective than other forms of capturing and analyzing student feedback, I needed to (1) examine the formative feedback literature and (2) test DropThought style feedback against traditional feedback forms. The method and theory proposed demonstrates that feedback from students can be specific and actionable when captured in the moment at students' activity level, in their own words. Application of the real-time feedback approach are relevant to practitioners and researchers alike, whether an instructor looking to improve her class activities, or a learning scientist carrying out interventionist, design-based research.

The purpose of this paper is to give a log-linear representation of a generalized Bradley-Terry (BT-) Model for paired comparisons which allows the incorporation of ties, order effects, concomitant variables for the objects and categorical subject specific covariates and interactions between all of them. An advantage of this approach is that standard software for fitting log-linear models, such as GLIM, can be used. The approach is exemplified by analysing data from an experiment concerning the ranking of European universities. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

<p>This dissertation explores how consumers learn from repeated experiences with a product offering. It develops a new Bayesian consumer learning model, the unstable learning model. This model expands on existing models that explore learning when quality is stable, by considering when quality is changing. Further, the dissertation examines situations in which consumers may act as if quality is changing when it is stable or vice versa. This examination proceeds in two essays.</p><p>The first essay uses two experiments to examine how consumers learn when product quality is stable or changing. By collecting repeated measures of expectation data and experiences, more information enables estimation to discriminate between stable and unstable learning. The key conclusions are that (1) most consumers act as if quality is unstable, even when it is stable, and (2) consumers respond to the environment they face, adjusting their learning in the correct direction. These conclusions have important implications for the formation and value of brand equity.</p><p>Based on the conclusions of this first essay, the second essay develops a choice model of consumer learning when consumers believe quality is changing, even though it is not. A Monte Carlo experiment tests the efficacy of this model versus the standard model. The key conclusion is that both models perform similarly well when the model assumptions match the way consumers actually learn, but with a mismatch the existing model is biased, while the new model continues to perform well. These biases could lead to suboptimal branding decisions.</p> / Dissertation