Se propusieron varias metodologías para analizar datos longitudinales (en forma univariante, mediante MANOVA, en curvas de crecimiento y bajo respuesta no normal mediante modelos lineales generalizados) usando distancias entre observaciones (o individuos) con respecto a las variables explicativas con variables respuesta de tipo continuo. En todas las metodologías propuestas al agregar más componentes de la matriz de coordenadas principales se encuentra que se gana en las predicciones con respecto a los modelos clásicos. Por lo cual resulta ser una metodología alternativa frente a la clásica para realizar predicciones. Se probó que el modelo MANOVA con DB y la aproximación univariante longitudinal con DB generan resultados tan robustos como la aproximación de MANOVA clásica y univariante clásica para datos longitudinales, haciendo uso en la aproximación clásica de máxima verosimilitud restringida y mínimos cuadrados ponderados bajo condiciones de normalidad. Los parámetros del modelo univariante con DB fueron estimados por el método de máxima verosimilitud restringida y por mínimos cuadrados generalizados. Para la aproximación MANOVA con DB se uso mínimos cuadrados bajo condiciones de normalidad. Además, se presentó como realizar inferencia sobre los parámetros involucrados en el modelo para muestras grandes. Se explicó también una metodología para analizar datos longitudinales mediante modelos lineales generalizados con distancias entre observaciones con respecto a las variables explicativas, donde se encontraron resultados similares a la metodología clásica y la ventaja de poder modelar datos de respuesta continua no normal en el tiempo. Inicialmente, se presenta el modelo propuesto, junto con las ideas principales que dan su origen, se realiza la estimación de parámetros y el contraste de hipótesis. La estimación se hace aplicando la metodología de ecuaciones de estimación generalizada (EEG). Por medio de una aplicación en cada capítulo se ilustraron las metodologías propuestas. Se ajusto el modelo, se obtuvo la estimación de los diferentes parámetros involucrados, se realizó la inferencia estadística del modelo propuesto y la validación del modelo propuesto. Pequeñas diferencias del método DB con respecto al clásico fueron encontradas en el caso de datos mixtos, especialmente en muestras pequeñas de tamaño 50, resultado obtenido de la simulación. Mediante simulación para algunos tamaños de muestra se encontró que el modelo ajustado DB produce mejores predicciones en comparación con la metodología tradicional para el caso en que las variables explicativas sean mixtas utilizando la distancia de Gower. En tamaños de muestras pequeñas 50, independiente del valor de la correlación, las estructuras de autocorrelación, la varianza y el número de tiempos, usando los criterios de información Akaike y Bayesiano (AIC y BIC). Además, para muestras pequeñas de tamaño 50 se encuentra más eficiente (eficiencia mayor a 1) el método DB en comparación con el método clásico, bajo los diferentes escenarios considerados. Otro resultado importante es que el método DB presenta mejor ajuste en muestras grandes (100 y 200), con correlaciones altas (0.5 y 0.9), varianza alta (50) y mayor número de mediciones en el tiempo (7 y 10). Cuando las variables explicativas son solamente de tipo continuo o categórico o binario, se probó que las predicciones son las mismas con respecto al método clásico. Adicionalmente, se desarrollaron los programas en el software R para el análisis de este tipo de datos mediante la metodología clásica y por distancias DB para las diferentes propuestas en cada uno de los capítulos de la tesis, los cuales se anexan en un CD dentro de la tesis. Se esta trabajando en la creación de una librería en R con lo ya programado, para que todos los usuarios tengan acceso a este tipo de análisis. Los métodos propuestos tienen la ventaja de poder hacer predicciones en el tiempo, se puede modelar la estructura de autocorrelación, se pueden modelar datos con variables explicativas mixtas, binarias, categóricas o continuas, y se puede garantizar independencia en las componentes de la matriz de coordenadas principales mientras que con las variables originales no se puede garantizar siempre independencia. Por último, el método propuesto produce buenas predicciones para estimar datos faltantes, ya que al agregar una o más componentes en el modelo con respecto a las variables explicativas originales de los datos, se puede mejorar el ajuste sin alterar la información original y por consiguiente resulta ser una buena alternativa para el análisis de datos longitudinales y de gran utilidad para investigadores cuyo interés se centra en obtener buenas predicciones. / LONGITUDINAL AND MULTIVARIATE DATA ANALYSIS THROUGH DISTANCES WITH GENERALIZED LINEAR MODELS We are introducing new methodologies for the analysis of longitudinal data with continuous responses (univariate, multivariate for growth curves and with non-normal response using generalized linear models) based on distances between observations (or individuals) on the explicative variables. In all cases, after adding new components of the principal coordinate matrix, we observe a prediction improvement with respect to the classic models, thus providing an alternative prediction methodology to them. It was proven that both the distance based MANOVA model and the univariate longitudinal models are as robust as the classical counterparts using restricted maximum likelihood and weighted minimum squares under normality assumptions. The parameters of the distance based univariate model were estimated using restricted maximum likelihood and generalized minimum squares. For the distance based MANOVA we used minimum squares under normality conditions. We also showed how to perform inference on the model parameters on large samples. We indicated a methodology for the analysis of longitudinal data using generalized linear models and distances between the explanatory variables, where the results were similar to the classical approach. However, our approach allowed us to model continuous, non-normal responses in the time. As well as presenting the model and the motivational ideas, we indicate how to estimate the parameters and hypothesis test on them. For this purpose we use generalized estimating equations (EEG). We present an application case in each chapter for illustration purposes. The models were fit and validated. After performing some simulations, we found small differences in the distance based method with respect to the classical one for mixed data, particularly in the small sample setting (about 50 individuals). Using simulation we found that for some sample sizes, the distance based models improve the traditional ones when explanatory variables are mixed and Gower distance is used. This is the case for small samples, regardless of the correlation, autocorrelation structure, the variance, and the number of periods when using both the Akaike (AIC) and Bayesian (BIC) Information Criteria. Moreover, for these small samples, we found greater efficiency (>1) in our model with respect to the classical one. Our models also provide better fits in large samples (100 or 200) with high correlations (0.5 and 0.9), high variance (50) and larger number of time measurements (7 and 10). We proved that the new and the classical models coincide when explanatory variables are all either continuous or categorical (or binary). We also created programs in R for the analysis of the data considered in the different chapters of this thesis in both models, the classical and the newly proposed one, which are attached in a CD. We are currently working to create a public, accessible R package. The main advantages of these methods are that they allow for time predictions, the modelization of the autocorrelation structure, and the analysis of data with mixed variables (continuous, categorical and binary). In such cases, as opposed to the classical approach, the independency of the components principal coordinate matrix can always be guaranteed. Finally, the proposed models allow for good missing data estimation: adding extra components to the model with respect to the original variables improves the fit without changing the information original. This is particularly important in the longitudinal data analysis and for those researchers whose main interest resides in obtaining good predictions.

<p>This dissertation examines the use of personae, the rhetorical artifices by which an author creates different voices, in John Gower's Confessio Amantis. I argue that the Confessio attempts to expose how discourses of sexual desire alienate subjects from their proper place in the political world, and produce artificial personae that only appear socially engaged. The first three chapters consider the creation of the personae in the context of medieval Aristotelian political thought and the Roman de la Rose tradition. The last three chapters examine the extended discourse of Gower's primary personae in the Confessio Amantis, drawing upon Gower's other works and the history of Gower criticism.</p> / Dissertation

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

The stories we tell give meaning and coherence to our political situation; they reproduce, interrogate, and, at times, challenge the discourse of authority. Thus, when the political situation changes so do our narratives. In the thirteenth century, responding to a majestic rhetoric of vis et voluntas (force and will), the barons strengthened the community of the realm by turning it into a powerful collective identity that fostered political alliances with the gentry. By The Will of the King demonstrates how Ricardian poetry was shaped by and responded to the conflict between majestic and political rhetoric that crystallized in the politically turbulent years culminating in the Second Barons’ War (1258-1265). By placing Gower’s Confessio Amantis and Chaucer’s Canterbury Tales in dialogue with this political tradition, I demonstrate how narrative became a site of conflict between vertical, cosmic descriptions of power and horizontal realities of power, a conflict from which the contours of a civic habit of mind began to emerge. Over the past twenty years, scholars have begun to investigate the evolution of this habit of mind in the late Middle Ages. By looking at the narrative practice of Gower and Chaucer through the lens of thirteenth-century political innovation, I extend and fill in this depiction of a nascent political imaginary. Each poet responds to the new political circumstances in their own way. Gower, placing the political community at the center of Book VII of the Confessio, rigorously reworks the mirror for princes genre into a schematic analysis of political power. For Chaucer, political rhetoric becomes visible at the moment that the traditional majestic rhetoric of kingship collapses. The Canterbury Tales, as such, restages the conflict of the thirteenth century in aesthetic terms—giving form to the crisis of authority. Ultimately, Ricardian poetry exposes and works through an anxiety of sovereignty; it registers the limits of a majestic paradigm of kingship; and reshaping narrative, aesthetic, and hermeneutic practice, it conjures a new political imaginary capable of speaking to and for a community which had emerged during the reign of Henry III.

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Sorting out data into partitions is increasing becoming complex as the constituents of data is growing outward everyday. Mixed data comprises continuous, categorical, directional functional and other types of variables. Clustering mixed data is based on special dissimilarities of the variables. Some data types may influence the clustering solution. Assigning appropriate weight to the functional data may improve the performance of the clustering algorithm. In this paper we use the extension of the Gower coefficient with judciously chosen weight for the L2 to cluster mixed data.The benefits of weighting are demonstrated both in in applications to the Buoy data set as well simulation studies. Our studies show that clustering algorithms with application of proper weight give superior recovery level when a set of data with mixed continuous, categorical directional and functional attributes is clustered. We discuss open problems for future research in clustering mixed data.

Clustering is a widely used technique in data mining applications to source, manage, analyze and extract vital information from large amounts of data. Most clustering procedures are limited in their performance when it comes to data with mixed attributes. In recent times, mixed data have evolved to include directional and functional data. In this study, we will give an introduction to clustering with an eye towards the application of the extended Gower coefficient by Hendrickson (2014). We will conduct a simulation study to assess the performance of this coefficient on mixed data whose functional component has strictly-decreasing signal curves and also those whose functional component has a mixture of strictly-decreasing signal curves and periodic tendencies. We will assess how four different hierarchical clustering algorithms perform on mixed data simulated under varying conditions with and without weights. The comparison of the various clustering solutions will be done using the Rand Index.

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This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissimilarities between mixed data time series. The time series are then clustered with k−medoids and the clusters are evaluated with the silhouette score and t−SNE. The study further investigates the use of a time warping regularisation parameter. It is derived that implementing time as a feature has the same effect as penalising time warping, andtherefore time is implemented as a feature where the feature weight is equivalent to a regularisation parameter. The results show that the proposed method successfully identifies clusters in customer transaction data provided by Nordea. Furthermore, the results show a decrease in the silhouette score with an increase in the regularisation parameter, suggesting that the time at which a transaction occurred might not be of relevance to the given dataset. However, due to the method’s high computational complexity, it is limited to relatively small datasets and therefore a need exists for a more scalable and efficient clustering technique. / Denna uppsats utforskar klustring av ojämnt fördelade tidsserier med numeriska och kategoriska variabler för kundsegmentering. Den föreslagna metoden implementerar Gower dissimilaritet som avståndsfunktionen i dynamic time warping för att beräkna dissimilaritet mellan tidsserierna. Tidsserierna klustras sedan med k-medoids och klustren utvärderas med silhouette score och t-SNE. Studien undersökte vidare användningen av en regulariserings parameter. Det härledes att implementering av tid som en egenskap hade samma effekt som att bestraffa dynamic time warping, och därför implementerades tid som en egenskap där dess vikt är ekvivalent med en regulariseringsparameter.  Resultaten visade att den föreslagna metoden lyckades identifiera kluster i transaktionsdata från Nordea. Vidare visades det att silhouette score minskade då regulariseringsparametern ökade, vilket antyder att tiden transaktion då en transaktion sker inte är relevant för det givna datan. Det visade sig ytterligare att metoden är begränsad till reltaivt små dataset på grund av dess höga beräkningskomplexitet, och därför finns det behov av att utforksa en mer skalbar och effektiv klusteringsteknik.