This thesis shows how to test the fit of a data set to a number of different models, using Watson’s U2 statistic for both grouped and continuous data. While Watson’s U2 statistic was introduced for continuous data, in recent work, the statistic has been adapted for grouped data. However, when using Watson’s U2 for continuous data, the asymptotic distribution is difficult to obtain, particularly, for some skewed circular distributions that contain four or five parameters. Until now, U2 asymptotic points are worked out only for uniform distribution and the von Mises distribution among all circular distributions. We give U2 asymptotic points for the wrapped exponential distributions, and we show that U2 asymptotic points when data are grouped is usually easier to obtain for other more advanced circular distributions. In practice, all continuous data is grouped into cells whose width is decided by the accuracy of the measurement. It will be found useful to treat such data as grouped with sufficient number of cells in the examples to be analyzed. When the data are treated as grouped, asymptotic points for U2 match well with the points when the data are treated as continuous. Asymptotic theory for U2 adopted for grouped data is given in the thesis. Monte Carlo studies show that, for reasonable sample sizes, the asymptotic points will give good approximations to the p-values of the test.

Made available in DSpace on 2014-12-17T15:26:38Z (GMT). No. of bitstreams: 1 Patricia Borchardt Santos.pdf: 378137 bytes, checksum: e27ccc5c056aa17d7bd2ca2c8b64458f (MD5) Previous issue date: 2009-12-01 / We presented in this work two methods of estimation for accelerated failure time models with random e_ects to process grouped survival data. The _rst method, which is implemented in software SAS, by NLMIXED procedure, uses an adapted Gauss-Hermite quadrature to determine marginalized likelihood. The second method, implemented in the free software R, is based on the method of penalized likelihood to estimate the parameters of the model. In the _rst case we describe the main theoretical aspects and, in the second, we briey presented the approach adopted with a simulation study to investigate the performance of the method. We realized implement the models using actual data on the time of operation of oil wells from the Potiguar Basin (RN / CE). / Apresentamos neste trabalho dois m?todos de estima??o para modelos de tempo de falha acelerado com efeito aleat?rio para tratar de dados de sobreviv?ncia correlacionados. O primeiro m?todo, que est? implementado no software SAS, atrav?s do procedimento NLMIXED, utiliza a quadratura Gauss-Hermite adaptada para obter a verossimilhan?a marginalizada. O segundo m?todo, implementado no software livre R, est? baseado no m?todo da verossimilhan?a penalizada para estimar os par?metros do modelo. No primeiro caso descrevemos os principais aspectos te?ricos e, no segundo, apresentamos brevemente a abordagem adotada juntamente com um estudo de simula??o para investigar a performance do m?todo. Realizamos uma aplica??o dos modelos usando dados reais sobre o tempo de funcionamento de po?os petrol?feros da Bacia Potiguar (RN/CE).

Cette thèse vise l’étude des processus d’émergence, de structuration et de mutation d’un réseau urbain antique depuis la fin de la Protohistoire jusqu’au Moyen Âge dans une micro-région située aux confins des territoires antiques éduens, lingons et séquanes, le Val de Saône. Celle-ci se caractérise par une armature dense d’agglomérations gallo-romaines possédant une somme et une qualité de données inégalée en Gaule, fruit de recherches anciennes et récentes. Alors que ces agglomérations ont été parmi les premières étudiées au cours des années 1980, leur exploitation s’est limitée à l’étude des hiérarchies et des fonctions urbaines durant le Haut-Empire, délaissant la chronologie et l’évolution du système urbain.Le corpus de sites a fait l’objet d’une démarche d’homogénéisation et de cartographie des données grâce à des outils spécialement développés. Pour répondre aux questions d’organisation spatiale et de chronologie, de nouvelles acquisitions d’informations ont été réalisées. Ces données, rassemblées dans des notices et synthétisées à travers une série de descripteurs, permettent de réfléchir sur une meilleure définition de la caractérisation des agglomérations et sur l’étude de leur parcours chronologique.C’est à partir de la typo-chronologie des agglomérations que les dynamiques spatiales, morphologiques et temporelles sont mises en évidence à l’échelle du Val de Saône, puis comparées avec les données du Centre-Est de la Gaule. L’analyse in-terne des agglomérations permet d’appréhender la place et le rôle des composantes principales des occupations comme les parures monumentales, les types d’habitat ou l’artisanat. L’enquête montre l’importance et la variété des agglomérations dans le système de peuplement du Val de Saône depuis la fin de la période laténienne. L’organisation des ré-seaux antiques, issue d’un héritage protohistorique très marqué, et les dynamiques urbaines indiquent des récurrences et des spécificités à chacune des cités.Cette thèse constitue ainsi une synthèse inédite sur l’émergence, le développement et le devenir de ce réseau urbain remarquable, dont la structure est encore nettement discernable aujourd’hui. / The aim of this thesis is to study the processes of emergence, structuring and mutation of an ancient urban network from the end of the Protohistory to the beginning of the Middle Ages in a micro-region located on the edge of the ancient éduens, lingons and séquanes territories, the Saône Valley. This area is characterized by a density of Gallo-Roman small towns with an amount and a high quality of data unequalled in Gaul, as a result of ancient and recent research. While these small towns were among the first studied in the 1980’s, their exploitation was limited to the study of hierarchies and urban functions during the High Empire, neglecting the chronology and the evolution of the urban system.The site corpus has been homogenized and mapped using new tools that were specially developed for that. To meet the expectations of spatial organization and chronology, new information has been gathered. These data, collected in notes and synthesised through a set of archaeological descriptors, allow us to reflect on a better definition of the small towns characterization and on the study of their evolution paths.It is from the typo-chronology of the towns that spatial, morphological and temporal dynamics are highlighted on the scale of the Saône Valley, then are compared to the Central-East of Gaul’s data. The internal analysis of towns makes it possible to understand the place and role of the main components of occupancies such as monumental adornments, habitat types or handicraft. The research shows the importance and the variety of the urban area towns in the settlement system of Saône Valley from the end of the Latenian period. The organization of ancient networks, resulting from a very marked protohistorical heritage, and the urban dynamics indicate recurrences and specificities to each of the cities.This thesis makes an unprecedented synthesis on the emergence, development and future of this remarkable urban network, whose structure is still distinctly discernible nowadays.

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This paper deals with CUSUM tests based on grouped or classified observations. The computation of average run length is reduced to that of solving of a system of simultaneous linear equations. Moreover a corresponding approximation based on the Wald approximations for characteristics of sequential likelihood ratio tests is presented. The effect of grouping is investigated with a CUSUM test for the mean of a normal distribution based on F-optimal grouping schemes. The considered example demonstrates that hight efficient CUSUM tests can be obtained for F-optimal grouping schemes already with a small number of groups.

This paper deals with sequential likelihood ratio tests based on grouped observations. It is demonstrated that the method of conjugated parameter pairs known from the non-grouped case can be extended to the grouped case obtaining Waldlike approximations for the OC- and ASN- function. For near hypotheses so-called F-optimal groupings are recommended. As example an SPRT based on grouped observations for the parameter of an exponentially distributed random variable is considered.

Les Séries Temporelles d'Images Satellitaires (STIS), visant la même scène en évolution, sont très intéressantes parce qu'elles acquièrent conjointement des informations temporelles et spatiales. L'extraction de ces informations pour aider les experts dans l'interprétation des données satellitaires devient une nécessité impérieuse. Dans ce mémoire, nous exposons comment on peut adapter l'extraction de motifs séquentiels fréquents à ce contexte spatio-temporel dans le but d'identifier des ensembles de pixels connexes qui partagent la même évolution temporelle. La démarche originale est basée sur la conjonction de la contrainte de support avec différentes contraintes de connexité qui peuvent filtrer ou élaguer l'espace de recherche pour obtenir efficacement des motifs séquentiels fréquents groupés (MSFG) avec signification pour l'utilisateur. La méthode d'extraction proposée est non supervisée et basée sur le niveau pixel. Pour vérifier la généricité du concept de MSFG et la capacité de la méthode proposée d'offrir des résultats intéressants à partir des SITS, sont réalisées des expérimentations sur des données réelles optiques et radar. / The Satellite Image Time Series (SITS), aiming the same scene in evolution, are of high interest as they capture both spatial and temporal information. The extraction of this infor- mation to help the experts interpreting the satellite data becomes a stringent necessity. In this work, we expound how to adapt frequent sequential patterns extraction to this spatiotemporal context in order to identify sets of connected pixels sharing a same temporal evolution. The original approach is based on the conjunction of support constraint with different constraints based on pixel connectivity that can filter or prune the search space in order to efficiently ob- tain Grouped Frequent Sequential (GFS) patterns that are meaningful to the end user. The proposed extraction method is unsupervised and pixel level based. To verify the generality of GFS-pattern concept and the proposed method capability to offer interesting results from SITS, real data experiments on optical and radar data are presented.

Au-delà du cadre classique de la production du logement, des projets originaux à l’initiative de citoyens ou impliquant fortement des habitants, se développent sur le territoire européen et notamment français. S‘émancipant de l’offre professionnelle privée ou publique, ces maîtrises d’ouvrages d’usagers produisent un habitat original, tant du point de vue architectural et environnemental, que de l’insertion sociale et au territoire qui l’accueille. Ce type de dynamique n’est pas un phénomène nouveau. En Amérique Latine notamment, des mouvements populaires et coopératifs développent ce type de démarche depuis plusieurs décennies. Insuffisamment étudié, ce phénomène mérite d’être analysé plus précisément, en particulier dans le champ de l’architecture et du point de vue des porteurs de ces projets : les habitants. Cette recherche vise à montrer, par une analyse des différentes typologies d’organisations collectives, adoptées par ces maîtrises d’ouvrage non professionnelles d’habitants, les atouts et les résultats de leur production, du point de vue de : la question « spatiale », qui interroge la notion de l’habitat et de son usage ; la question du « métier » du concepteur, qui interroge le mode de production du projet au travers de la relation architecte – maîtrise d’ouvrage (non professionnelle) ; et enfin la question de « l’insertion spatiale et sociale », qui interroge la dimension urbaine de ces projets et citoyenne de leurs promoteurs. Se basant sur les notions de « droit à l’œuvre » et de « droit à la ville » définies par Henri Lefebvre et de « bien commun » définie par Elinor Ostrom, cette thèse propose un apport de connaissances visant à contribuer à la réponse des professionnels et des politiques publiques, à une demande sociale chaque jour plus présente en matière d’initiatives citoyennes dans la production de l’habitat. / Further to the production of housing within a traditional Framework are curently developed all over europe and specialy in France. Moving away from public or private standar models, the projects inspired by the end users tend to produce very original habitats, in terms of architecture and environment as well as social and territorial insertion. This kind of dynamic is not a new phenomenom. Particulary in Latin America, popular and cooperative movements have developed this type of approach for decades. Insufficiently studied, this phenomenom merits more precise analysis, particulary in the field of architecture and from the point of view of the managers of such projects : the inhabitants. This research aims to demonstrate, through an analysis of different types of collective organizations adopted by these non-professional inhabitants leadership, the benefits and results of their production, in terms of: the "spacial" question, which looks at the concept of habitat and its use ; the issue of "skill" of the designer, who questions the mode of production of the project through the architect relationship - project management (non-occupational) ; and finally the area of "spatial and social inclusion", which looks at the urban dimension of these projects and their "property developers". Based on the concepts of "right to do" and "right to the city" defined by Henri Lefebvre and "common good" defined by Elinor Ostrom, this thesis provides a contribution of knowledge to contribute to the response of professionals and public policy, to a real social demand present every day in terms of citizen initiatives in the production of housing.

Hierarchical Bayesian Models and Matrix factorization methods provide an unsupervised way to learn latent components of data from the grouped or sequence data. For example, in document data, latent component corn-responds to topic with each topic as a distribution over a note vocabulary of words. For many applications, there exist sparse relationships between the domain entities and the latent components of the data. Traditional approaches for topic modelling do not take into account these sparsity considerations. Modelling these sparse relationships helps in extracting relevant information leading to improvements in topic accuracy and scalable solution. In our thesis, we explore these sparsity relationships for di errant applications such as text segmentation, topical analysis and entity resolution in dyadic data through the Bayesian and Matrix tri-factorization approaches, propos-in scalable solutions. In our rest work, we address the problem of segmentation of a collection of sequence data such as documents using probabilistic models. Existing state-of-the-art Hierarchical Bayesian Models are connected to the notion of Complete Exchangeability or Markov Exchangeability. Bayesian Nonpareil-metric Models based on the notion of Markov Exchangeability such as HDP-HMM and Sticky HDP-HMM, allow very restricted permutations of latent variables in grouped data (topics in documents), which in turn lead to com-mutational challenges for inference. At the other extreme, models based on Complete Exchangeability such as HDP allow arbitrary permutations within each group or document, and inference is significantly more tractable as a result, but segmentation is not meaningful using such models. To over-come these problems, we explored a new notion of exchangeability called Block Exchangeability that lies between Markov Exchangeability and Com-plate Exchangeability for which segmentation is meaningful, but inference is computationally less expensive than both Markov and Complete Exchange-ability. Parametrically, Block Exchangeability contains sparser number of transition parameters, linear in number of states compared to the quadratic order for Markov Exchangeability that is still less than that for Complete Exchangeability and for which parameters are on the order of the number of documents. For this, we propose a nonparametric Block Exchangeable model (BEM) based on the new notion of Block Exchangeability, which we have shown to be a superclass of Complete Exchangeability and subclass of Markov Exchangeability. We propose a scalable inference algorithm for BEM to infer the topics for words and segment boundaries associated with topics for a document using the collapsed Gibbs Sampling procedure. Empirical results show that BEM outperforms state-of-the-art nonparametric models in terms of scalability and generalization ability and shows nearly the same segmentation quality on News dataset, Product review dataset and on a Synthetic dataset. Interestingly, we can tune the scalability by varying the block size through a parameter in our model for a small trade-o with segmentation quality. In addition to exploring the association between documents and words, we also explore the sparse relationships for dyadic data, where associations between one pair of domain entities such as (documents, words) and as-associations between another pair such as (documents, users) are completely observed. We motivate the analysis of such dyadic data introducing an additional discrete dimension, which we call topics, and explore sparse relation-ships between the domain entities and the topic, such as of user-topic and document-topic respectively. In our second work, for this problem of sparse topical analysis of dyadic data, we propose a formulation using sparse matrix tri-factorization. This formulation requires sparsity constraints, not only on the individual factor matrices, but also on the product of two of the factors. To the best of our knowledge, this problem of sparse matrix tri-factorization has not been stud-ide before. We propose a solution that introduces a surrogate for the product of factors and enforces sparsity on this surrogate as well as on the individual factors through L1-regularization. The resulting optimization problem is e - cogently solvable in an alternating minimization framework over sub-problems involving individual factors using the well-known FISTA algorithm. For the sub-problems that are constrained, we use a projected variant of the FISTA algorithm. We also show that our formulation leads to independent sub-problems towards solving a factor matrix, thereby supporting parallel implementation leading to a scalable solution. We perform experiments over bibliographic and product review data to show that the proposed framework based on sparse tri-factorization formulation results in better generalization ability and factorization accuracy compared to baselines that use sparse bi-factorization. Even though the second work performs sparse topical analysis for dyadic data, ending sparse topical associations for the users, the user references with di errant names could belong to the same entity and those with same names could belong to different entities. The problem of entity resolution is widely studied in the research community, where the goal is to identify real users associated with the user references in the documents. Finally, we focus on the problem of entity resolution in dyadic data, where associations between one pair of domain entities such as documents-words and associations between another pair such as documents-users are ob.-served, an example of which includes bibliographic data. In our nil work, for this problem of entity resolution in bibliographic data, we propose a Bayesian nonparametric `Sparse entity resolution model' (SERM) exploring the sparse relationships between the grouped data involving grouping of the documents, and the topics/author entities in the group. Further, we also exploit the sparseness between an author entity and the associated author aliases. Grouping of the documents is achieved with the stick breaking prior for the Dirichlet processes (DP). To achieve sparseness, we propose a solution that introduces separate Indian Bu et process (IBP) priors over topics and the author entities for the groups and k-NN mechanism for selecting author aliases for the author entities. We propose a scalable inference for SERM by appropriately combining partially collapsed Gibbs sampling scheme in Focussed topic model (FTM), the inference scheme used for parametric IBP prior and the k-NN mechanism. We perform experiments over bibliographic datasets, Cite seer and Rexa, to show that the proposed SERM model imp-proves the accuracy of entity resolution by ending relevant author entities through modelling sparse relationships and is scalable, when compared to the state-of-the-art baseline

This paper deals with CUSUM tests based on grouped or classified observations. The computation of average run length is reduced to that of solving of a system of simultaneous linear equations. Moreover a corresponding approximation based on the Wald approximations for characteristics of sequential likelihood ratio tests is presented. The effect of grouping is investigated with a CUSUM test for the mean of a normal distribution based on F-optimal grouping schemes. The considered example demonstrates that hight efficient CUSUM tests can be obtained for F-optimal grouping schemes already with a small number of groups.