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Fridelance, Patricia. "L'experience lasso." Paris 6, 1994. http://www.theses.fr/1994PA066575.
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L'experience lasso: laser synchronisation from stationary orbit est une procedure de transfert de temps entre des horloges au sol utilisant un lien laser avec un oscillateur ultra-stable embarque a bord d'un satellite geostationnaire. L'objet de cette these est de faire un bilan complet de cette experience, de sa proposition aux experiences futures qui en decoulent, en passant par l'analyse detaillee des observations. Cette experience a pour but de montrer la faisabilite d'un transfert de temps intercontinental a mieux que la nanoseconde (10#-#9s). Un tel transfert de temps a pu etre realise grace a l'introduction de nouveaux concepts: le suivi de l'horloge du satellite de facon independante a partir de chaque station, et l'utilisation de toute l'information disponible dans les donnees. Durant 1992 et 1993, deux stations ont participe a cette experience: le mlrs (texas) et le cerga (grasse). Le traitement et l'analyse de ces donnees ont montre que lasso permet d'etudier le comportement d'une horloge embarquee sur un satellite avec une precision meilleure que 100 ps (10#-#1#0s), et que la precision du transfert de temps via lasso est meilleure que 100 picosecondes. Il y a donc un gain d'un facteur 5 a 10 par rapport aux techniques de transfert de temps actuelles (gps, two-way). L'exactitude, actuellement de l'ordre de 1. 5 ns, pourra etre amelioree par un meilleur etalonnage des stations. Pour l'avenir, une experience de type lasso est envisageable avec une precision d'une dizaine de picosecondes, ce qui presente un grand interet dans le domaine de la metrologie du temps pour l'etude d'horloges ultra-stables en orbite, l'etalonnage de techniques de transfert de temps. Cela ouvre egalement la voie a des experiences de physique fondamentale dans le systeme solaire
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Jürgens, Henning P. "Johannes a Lasco in Ostfriesland : der Werdegang eines europäischen Reformators /." Tübingen : Mohr Siebeck, 2002. http://catalogue.bnf.fr/ark:/12148/cb38889468d.
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Becker, Judith. "Gemeindeordnung und Kirchenzucht : Johannes a Lascos Kirchenordnung für London (1555) und die reformierte Konfessionsbildung /." Leiden : Brill, 2007. http://catalogue.bnf.fr/ark:/12148/cb411386407.
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Patnaik, Kaushik. "Adaptive learning in lasso models." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54353.
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Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern (also known as model selection) in linear models from observations contaminated by noise. We examine a scenario where a fraction of the zero co-variates are highly correlated with non-zero co-variates making sparsity recovery difficult. We propose two methods that adaptively increment the regularization parameter to prune the Lasso solution set. We prove that the algorithms achieve consistent model selection with high probability while using fewer samples than traditional Lasso. The algorithm can be extended to a broad set of L1-regularized M-estimators for linear statistical models.
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Han, Yuchen. "Bayesian Variable Selection Using Lasso." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1491775118610981.
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Vibert, Didier. "Modélisation et reconstruction tridimensionnelle de la couronne solaire dans le cadre de l'expérience LASCO." Aix-Marseille 3, 1997. http://www.theses.fr/1997AIX30072.
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Cette these presente le developpement de deux methodes d'etude de l'aspect tridimensionnel de la couronne solaire, dans le cadre de l'experience coronographique lasco, qui observe continuement la couronne depuis fevrier 1996. La premiere methode est une simulation des images en lumiere blanche a partir d'une description tridimensionnelle de la densite electronique du plasma coronal. Un echantillonnage non-uniforme de l'espace par la methode arborescente des octree a ete utilise pour cette description. Le gain ainsi realise sur la taille de la representation numerique de la densite electronique ainsi que sur le temps de calcul des images a rendu possible la synthese de sequences animees couvrant une rotation solaire. La comparaison de ces sequences avec les sequences de prises de vues de lasco a montre que nous avions reproduit correctement certaines des caracteristiques tridimensionnelles de la couronne solaire et notamment sa structure en forme de feuillet plisse. La deuxieme methode developpee consiste en un code d'inversion capable de reconstruire la densite electronique volumique a partir d'un ensemble de cliches coronographiques de lasco couvrant une rotation solaire. Ce code est une adaptation des algorithmes dits algebriques a ce cas de tomographie. Les contraintes d'observation en vue de l'utilisation de ce code ont ete degagees puis les limites et potentialites de la methode ont ete precisees en l'eprouvant a l'aide des images synthetisees.
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Fang, Yanling. "Synthèse d'images de la couronne solaire et simulation intrumentale du coronographe spatial LASCO/C2." Aix-Marseille 3, 1992. http://www.theses.fr/1992AIX30084.
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Ounaissi, Daoud. "Méthodes quasi-Monte Carlo et Monte Carlo : application aux calculs des estimateurs Lasso et Lasso bayésien." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10043/document.
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La thèse contient 6 chapitres. Le premier chapitre contient une introduction à la régression linéaire et aux problèmes Lasso et Lasso bayésien. Le chapitre 2 rappelle les algorithmes d’optimisation convexe et présente l’algorithme FISTA pour calculer l’estimateur Lasso. La statistique de la convergence de cet algorithme est aussi donnée dans ce chapitre en utilisant l’entropie et l’estimateur de Pitman-Yor. Le chapitre 3 est consacré à la comparaison des méthodes quasi-Monte Carlo et Monte Carlo dans les calculs numériques du Lasso bayésien. Il sort de cette comparaison que les points de Hammersely donne les meilleurs résultats. Le chapitre 4 donne une interprétation géométrique de la fonction de partition du Lasso bayésien et l’exprime en fonction de la fonction Gamma incomplète. Ceci nous a permis de donner un critère de convergence pour l’algorithme de Metropolis Hastings. Le chapitre 5 présente l’estimateur bayésien comme la loi limite d’une équation différentielle stochastique multivariée. Ceci nous a permis de calculer le Lasso bayésien en utilisant les schémas numériques semi implicite et explicite d’Euler et les méthodes de Monte Carlo, Monte Carlo à plusieurs couches (MLMC) et l’algorithme de Metropolis Hastings. La comparaison des coûts de calcul montre que le couple (schéma semi-implicite d’Euler, MLMC) gagne contre les autres couples (schéma, méthode). Finalement dans le chapitre 6 nous avons trouvé la vitesse de convergence du Lasso bayésien vers le Lasso lorsque le rapport signal/bruit est constant et le bruit tend vers 0. Ceci nous a permis de donner de nouveaux critères pour la convergence de l’algorithme de Metropolis HastingsThe thesis contains 6 chapters. The first chapter contains an introduction to linear regression, the Lasso and the Bayesian Lasso problems. Chapter 2 recalls the convex optimization algorithms and presents the Fista algorithm for calculating the Lasso estimator. The properties of the convergence of this algorithm is also given in this chapter using the entropy estimator and Pitman-Yor estimator. Chapter 3 is devoted to comparison of Monte Carlo and quasi-Monte Carlo methods in numerical calculations of Bayesian Lasso. It comes out of this comparison that the Hammersely points give the best results. Chapter 4 gives a geometric interpretation of the partition function of the Bayesian lasso expressed as a function of the incomplete Gamma function. This allowed us to give a convergence criterion for the Metropolis Hastings algorithm. Chapter 5 presents the Bayesian estimator as the law limit a multivariate stochastic differential equation. This allowed us to calculate the Bayesian Lasso using numerical schemes semi-implicit and explicit Euler and methods of Monte Carlo, Monte Carlo multilevel (MLMC) and Metropolis Hastings algorithm. Comparing the calculation costs shows the couple (semi-implicit Euler scheme, MLMC) wins against the other couples (scheme method). Finally in chapter 6 we found the Lasso convergence rate of the Bayesian Lasso when the signal / noise ratio is constant and when the noise tends to 0. This allowed us to provide a new criteria for the convergence of the Metropolis algorithm Hastings
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Mak, Carmen. "Polychotomous logistic regression via the Lasso." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0004/NQ41227.pdf.
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Loth, Manuel. "Algorithmes d'Ensemble Actif pour le LASSO." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00845441.
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Cette thèse aborde le calcul de l'opérateur LASSO (Least Absolute Shrinkage and Selection Operator), ainsi que des problématiques qui lui sont associées, dans le domaine de la régression. Cet opérateur a suscité une attention croissante depuis son introduction par Robert Tibshirani en 1996, par sa capacité à produire ou identi fier des modèles linéaires parcimonieux à partir d'observations bruitées, la parcimonie signi fiant que seules quelques unes parmi de nombreuses variables explicatives apparaissent dans le modèle proposé. Cette sélection est produite par l'ajout à la méthode des moindres-carrés d'une contrainte ou pénalisation sur la somme des valeurs absolues des coe fficients linéaires, également appelée norme l1 du vecteur de coeffi cients. Après un rappel des motivations, principes et problématiques de la régression, des estimateurs linéaires, de la méthode des moindres-carrés, de la sélection de modèle et de la régularisation, les deux formulations équivalentes du LASSO contrainte ou régularisée sont présentées; elles dé finissent toutes deux un problème de calcul non trivial pour associer un estimateur à un ensemble d'observations et un paramètre de sélection. Un bref historique des algorithmes résolvant ce problème est dressé, et les deux approches permettant de gérer la non-di fferentiabilité de la norme l1 sont présentées, ainsi que l'équivalence de ces problèmes avec un programme quadratique. La seconde partie se concentre sur l'aspect pratique des algorithmes de résolution du LASSO. L'un d'eux, proposé par Michael Osborne en 2000, est reformulé. Cette reformulation consiste à donner une défi nition et explication générales de la méthode d'ensemble actif, qui généralise l'algorithme du simplex à la programmation convexe, puis à la spéci fier progressivement pour la programmation LASSO, et à adresser les questions d'optimisation des calculs algébriques. Bien que décrivant essentiellement le même algorithme que celui de Michael Osborne, la présentation qui en est faite ici a l'ambition d'en exposer clairement les mécanismes, et utilise des variables di fférentes. Outre le fait d'aider à mieux comprendre cet algorithme visiblement sous-estimé, l'angle par lequel il est présenté éclaire le fait nouveau que la même méthode s'applique naturellement à la formulation régularisée du LASSO, et non uniquement à la formulation contrainte. La populaire méthode par homotopie (ou LAR-LASSO, ou LARS) est ensuite présentée comme une dérivation de la méthode d'ensemble actif, amenant une formulation alternative et quelque peu simpli fiée de cet algorithme qui fournit les solutions du LASSO pour chaque valeur de son paramètre. Il est montré que, contrairement aux résultats d'une étude récente de Jerome H. Friedman, des implémentations de ces algorithmes suivant ces reformulations sont plus effi caces en terme de temps de calcul qu'une méthode de descente par coordonnées. La troisième partie étudie dans quelles mesures ces trois algorithmes (ensemble actif, homotopie, et descente par coordonnées) peuvent gérer certains cas particuliers, et peuvent être appliqués à des extensions du LASSO ou d'autres problèmes similaires. Les cas particuliers incluent les dégénérescences, comme la présence de variables lineairement dépendantes, ou la sélection/désélection simultanée de variables. Cette dernière problématique, qui était délaissée dans les travaux précédents, est ici expliquée plus largement et une solution simple et efficace y est apportée. Une autre cas particulier est la sélection LASSO à partir d'un nombre très large, voire infi ni de variables, cas pour lequel la méthode d'ensemble actif présente un avantage majeur. Une des extensions du LASSO est sa transposition dans un cadre d'apprentissage en ligne, où il est désirable ou nécessaire de résoudre le problème sur un ensemble d'observations qui évolue dans le temps. A nouveau, la flexibilité limitée de la méthode par homotopie la disquali fie au pro fit des deux autres. Une autre extension est l'utilisation de la pénalisation l1 sur d'autres fonction coûts que la norme l2 du résidu, ou en association avec d'autres pénalisations, et il est rappelé ou établi dans quelles mesures et de quelle façon chaque algorithme peut être transposé à ces problèmes.
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Matthews, Stephen John. "Volcanology, petrology and geochemistry of Lascar Volcano, northern Chile." Thesis, University College London (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283332.
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Lykou, Anastasia. "Sparse canonical correlation analysis using the Lasso." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533099.
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Gaye, Serigne Abib, and Serigne Abib Gaye. "Estimation bayésienne du lasso adaptatif pour l'issue." Master's thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/38275.
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Dans ce mémoire, on cherche à développer une nouvelle méthode d'estimation pour le lasso adaptatif pour l'issue en utilisant la machinerie bayésienne. L'hypothèse de recherche est que notre nouvelle méthode va beaucoup réduire la lourdeur computationnelle du lasso adaptatif pour l'issue. Notre méthode utilise les mêmes fondements théoriques que le lasso adaptatif pour l'issue. Elle remplit donc les conditions de la propriété d'oracle. Pour sa mise en ÷uvre, on ajuste d'abord un modèle du score de propension bayésien. Ensuite, on estime l'effet du traitement moyen par la pondération par l'inverse de la probabilité de traitement. Par ailleurs, nous considérons une distribution gamma pour le paramètre de régularisation qui nous permet de choisir ce paramètre à partir d'un ensemble continu, alors que le lasso adaptatif pour l'issue fréquentiste utilise une approche de validation croisée qui doit faire un choix parmi un ensemble discret de valeurs préspéciées. In ne, la méthode que nous avons développée répond bien à nos attentes, et permet donc de produire les inférences de façon beaucoup plus rapide. En effet, il a fallu seulement 41.298 secondes pour que cette méthode effectue les inférences, alors que 44.105 minutes ont été né- cessaires au lasso adaptatif pour l'issue. On espère que les idées développées dans ce mémoire vont contribuer signicativement à améliorer les méthodes de sélection de variables en inférence causale avec l'appui des techniques bayésiennes.Dans ce mémoire, on cherche à développer une nouvelle méthode d'estimation pour le lasso adaptatif pour l'issue en utilisant la machinerie bayésienne. L'hypothèse de recherche est que notre nouvelle méthode va beaucoup réduire la lourdeur computationnelle du lasso adaptatif pour l'issue. Notre méthode utilise les mêmes fondements théoriques que le lasso adaptatif pour l'issue. Elle remplit donc les conditions de la propriété d'oracle. Pour sa mise en ÷uvre, on ajuste d'abord un modèle du score de propension bayésien. Ensuite, on estime l'effet du traitement moyen par la pondération par l'inverse de la probabilité de traitement. Par ailleurs, nous considérons une distribution gamma pour le paramètre de régularisation qui nous permet de choisir ce paramètre à partir d'un ensemble continu, alors que le lasso adaptatif pour l'issue fréquentiste utilise une approche de validation croisée qui doit faire un choix parmi un ensemble discret de valeurs préspéciées. In ne, la méthode que nous avons développée répond bien à nos attentes, et permet donc de produire les inférences de façon beaucoup plus rapide. En effet, il a fallu seulement 41.298 secondes pour que cette méthode effectue les inférences, alors que 44.105 minutes ont été né- cessaires au lasso adaptatif pour l'issue. On espère que les idées développées dans ce mémoire vont contribuer signicativement à améliorer les méthodes de sélection de variables en inférence causale avec l'appui des techniques bayésiennes.
In this paper, we aim to develop a new estimation method for the outcome adaptive lasso using Bayesian machinery. The research hypothesis is that our new method will significantly reduce the computational burden of the outcome adaptive lasso. Our method uses the same theoretical foundation as the outcome adaptive lasso. It therefore meets the oracle properties. For its implementation, Bayesian propensity score model is first fitted. Next, the average treatment effect is estimated using inverse probability of treatment weights. In addition, we consider a gamma distribution for the regularisation parameter λ in order to choose this parameter over a continuous set, whereas the frequentist outcome adaptive lasso uses a cross-validation procedure that selects λ among a prespecified discrete set. In fine, the method we have developed meets our expectations, and therefore makes it possible to produce inferences much faster. Indeed, it took only 41.298 seconds for this method to yield inferences, while 44.105 minutes were required for the outcome adaptive lasso. We hope that the ideas developed in this paper will significantly contribute to improve methods for selecting variables in causal inference with the support of Bayesian techniques.
In this paper, we aim to develop a new estimation method for the outcome adaptive lasso using Bayesian machinery. The research hypothesis is that our new method will significantly reduce the computational burden of the outcome adaptive lasso. Our method uses the same theoretical foundation as the outcome adaptive lasso. It therefore meets the oracle properties. For its implementation, Bayesian propensity score model is first fitted. Next, the average treatment effect is estimated using inverse probability of treatment weights. In addition, we consider a gamma distribution for the regularisation parameter λ in order to choose this parameter over a continuous set, whereas the frequentist outcome adaptive lasso uses a cross-validation procedure that selects λ among a prespecified discrete set. In fine, the method we have developed meets our expectations, and therefore makes it possible to produce inferences much faster. Indeed, it took only 41.298 seconds for this method to yield inferences, while 44.105 minutes were required for the outcome adaptive lasso. We hope that the ideas developed in this paper will significantly contribute to improve methods for selecting variables in causal inference with the support of Bayesian techniques.
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Jaffer, Aaron. "Lascar mutiny in the age of sail, c.1780-1860." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/57893/.
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This thesis examines the diverse body of seafarers known as ‘lascars’. Scholarship devoted to lascars who were employed during the age of sail has tended to focus on the minority who stayed and settled in Britain. Studying lascars as migrants can have the effect of obscuring the time they spent afloat and often risks casting them in the role of victims. Historians have failed to address fully the issue of how these men resisted the oppressive conditions of their employment whilst at sea. This thesis draws upon a wealth of unused source material – including logbooks, seafaring diaries and judicial records – to reconstruct lascar unrest aboard British merchantmen operating in the Indian Ocean between 1780 and 1860. It uncovers a wide range of hitherto overlooked forms of agency amongst lascars. These include everyday acts of collective protest such as demonstrations, refusals to work, assaults on officers and disorderly religious festivals. Such tactics enabled lascars to exert considerable influence aboard ship by venting anger, resisting unpopular orders and gaining concessions from their superiors. They also serve to broaden our understanding of what constitutes a ‘mutiny’ and how this could vary considerably between different cultural contexts. This thesis also examines more serious forms of mutiny, during which lascar crews killed commanders, commandeered vessels, expropriated cargoes and overturned established shipboard social relations. Uprisings of this nature occupy an important place in the long history of lascar employment and add significantly to our understanding of the Indian Ocean world. The last eight decades of East India Company rule witnessed a spate of such incidents, before European expansion and steam navigation rendered them unfeasible. The documents generated by this form of mutiny also provide one of the very few means of recapturing a lascar voice from the archives. Mutiny in all its forms thus offers an unparalleled window onto the working lives of these seafarers and the unique wooden world they inhabited.
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Baczkowski, Mylène. "Amélioration du processus de déploiement d'une solution PLM par l'utilisation de cartes heuristiques et de persona : cas LASCOM." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14714/document.
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Cette thèse se place dans une dynamique de recherche d’amélioration des processus d’implémentation et de déploiement de solutions logicielles de type PLM dans les entreprises. Nous proposons une démarche complète de déploiement, centrée sur les acteurs de l’entreprise, qui s’appuie sur l’utilisation de deux outils jusque là peu usités pour répondre à ce type de problématique : les cartes heuristiques et les personas. Nous proposons d’utiliser la carte heuristique (ou map) comme support du projet et moteur de la réflexion et de la communication dans le cadre d’un projet. La map offre une nouvelle dimension à la définition de l’application et à la communication avec le client : une structure et une organisation dynamiques et « immédiates ». Elle permet de mettre en évidence l’organisation et les processus du client. Nous affinons notre proposition en travaillant aussi sur l’accompagnement et l’implication du client en plaçant les futurs utilisateurs au centre de nos préoccupations. Nous proposons une démarche complémentaire à la carte heuristique pour faire émerger des spécifications directement par le client : les personas. La carte heuristique permet d’obtenir un support unique pour suivre le cycle de vie du logiciel et sa construction repose sur l’approche descendante, tandis que persona, centré sur les utilisateurs et leur environnement, se base sur une approche ascendante. Nous obtenons ainsi une double exploration du système, ce qui offre une nouvelle dimension à la modélisation d’entreprises en vue de l’implantation d’une solution logicielle. Cette proposition améliore la pertinence et la qualité de l’analyse et de l’applicationThis thesis concerns research on PLM tools. We especially focus here on improvement of the deployment process of PLM tools in enterprises. We develop a methodology to help PLM software developers to design and deploy a PLM solution among their customers. Our proposition, centered on final users of PLM solution, is build around two unusual tools for enterprise modeling: mind map and persona. Mind map is used as a communication element between developer and customer during the entire project. Mind map is a common support to exchange data and encourage reflection. It offers a new dynamic and a new dimension in the definition of the PLM solution for customer and developer since it makes easier description of customer’s organization and process. We enrich our proposition with persona. Persona completes mind map and permits an easier identification of users’ needs. Such a tool allows us to be more efficient on accompaniment and implication of customer and users. Mind map is a unique support for software developer and customer to follow life cycle of the software. It is based on a top-down approach. Persona is centered on users in the company and on their environment. It is a bottom-up approach. Association of these tools allows us obtaining a double exploration of the system that provides a new dimension in enterprise modeling with a view to software deployment. This proposition increase pertinence and quality of the users’ needs analysis and of customer organization modeling. As a consequence it also improves design and deployment of the PLM solution which is closed to the users’ needs and well adapted to the company’s organization and processes
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Argonz, Raquel. "Purificação de rejeitos de lascas de quartzo das industrias de silicio." [s.n.], 2001. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264918.
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Orientador: Carlos K. SuzukiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica
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Resumo: O Brasil é na atualidade um dos principais produtores de silício para o mundo, sendo que a quantidade de quartzo extraído para a sua produção incluindo o ferro-silício, é da ordem de 2 milhões de toneladas/ano. Para a obtenção do quartzo destinado à redução carbotérmica em silício, nos diversos estágios de extração, britagem, seleção, transporte, e lavagem, cerca de 300.000 toneladas/ano de lascas de quartzo tomam-se rejeitos. Neste trabalho foi desenvolvida uma metodologia ambientalmente correta, denominada "quench-Ieaching" e "crush-leaching", que se utiliza da lixiviação aquosa para a purificação deste material. Os resultados mostram que ocorre uma remoção efetiva de impurezas majoritárias nas lascas de quartzo, tais como, AI, Fe, Na, K, Ca, Mn, ..., dando-lhe uma pureza de 99,9% de SI 'O IND 2'. Uma comparação com diversos insumos de quartzo produzidos no exterior para uso em tecnologia avançada, como para produção de sílica vítrea translucente e "fillers" de "micro-chips", revela que este material purificado com esta tecnologia toma-se de qualidade equivalente ao pó de quartzo internacional
Abstract: Nowadays, Brazil is one of the main silicon metal and iron-silicon producer in the world. But on the other hand, the amount of natural quartz that has been extracted for this purpose is up to 2 milliontons/year. The key-point is the large quantity of rejected quartz lascas, approximately 300,000 tons/year, generated during the various stages of extraction, crushing, selection, transportation, and washing. A new environrnentally mendly purification methodology denominated "quench-Ieaching" and "crush-leaching, that only uses aqueous leaching, has been developed. The result shows an effective elimination of major quartz impurities, such as Al, Fe, Na, K, Ca, Mn, ... , that transforms this rejected material into a 99.9% purity SI 'O IND 2'. The quality of this material is as high as the quartz powder commercially available in the intemational market for use as "fillers" and translucent silica glass raw material for semiconductor industries
Doutorado
Materiais e Processos de Fabricação
Doutor em Engenharia Mecânica
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Lewitzka, Steffen. "Contributions to the investigations of Lascar strong types in simple theories." Universidade Federal de Pernambuco, 2003. https://repositorio.ufpe.br/handle/123456789/1862.
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Previous issue date: 2003Lewitzka, Steffen; José Guerra Barreto de Queiroz, Ruy. Contributions to the investigations of Lascar strong types in simple theories. 2003. Tese (Doutorado). Programa de Pós-Graduação em Ciência da Computação, Universidade Federal de Pernambuco, Recife, 2003.
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Olaya, Bucaro Orlando. "Predicting risk of cyberbullying victimization using lasso regression." Thesis, Uppsala universitet, Statistiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.
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The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis is to specify a prediction model that can accurately predict the risk of cyberbullying victimization. The data used is from a survey conducted at five secondary schools in Pereira, Colombia. A logistic regression model with random effects is used to predict cyberbullying exposure. Predictors are selected by lasso, tuned by cross-validation. Covariates included in the study includes demographic variables, dietary habit variables, parental mediation variables, school performance variables, physical health variables, mental health variables and health risk variables such as alcohol and drug consumption. Included variables in the final model are demographic variables, mental health variables and parental mediation variables. Variables excluded in the final model includes dietary habit variables, school performance variables, physical health variables and health risk variables. The final model has an overall prediction accuracy of 88%.
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Sun, Xiang. "The Lasso and its implementation for neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0019/NQ45795.pdf.
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Bruns, Katharina. "Das deutsche weltliche Lied von Lasso bis Schein." Kassel Basel London New York, NY Praha Bärenreiter, 2008. http://d-nb.info/989361837/04.
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Laslo, Tanja [Verfasser]. "D-arabitol metabolism of Corynebacterium glutamicum / Tanja Laslo." Ulm : Universität Ulm. Fakultät für Naturwissenschaften, 2013. http://d-nb.info/1036215148/34.
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Gao, Di. "Bayesian Lasso Models – With Application to Sports Data." Diss., North Dakota State University, 2018. https://hdl.handle.net/10365/27949.
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Several statistical models were proposed by researchers to fulfill the objective of correctly predicting the winners of sports game, for example, the generalized linear model (Magel & Unruh, 2013) and the probability self-consistent model (Shen et al., 2015). This work studied Bayesian Lasso generalized linear models. A hybrid model estimation approach of full and Empirical Bayesian was proposed. A simple and efficient method in the EM step, which does not require sample mean from the random samples, was also introduced. The expectation step was reduced to derive the theoretical expectation directly from the conditional marginal. The findings of this work suggest that future application will significantly cut down the computation load. Due to Lasso (Tibshirani, 1996)’s desired geometric property, the Lasso method provides a sharp power in selecting significant explanatory variables and has become very popular in solving big data problem in the last 20 years. This work was constructed with Lasso structure hence can also be a good fit to achieve dimension reduction. Dimension reduction is necessary when the number of observations is less than the number of parameters or when the design matrix is non-full rank. A simulation study was conducted to test the power of dimension reduction and the accuracy and variation of the estimates. For an application of the Bayesian Lasso Probit Linear Regression to live data, NCAA March Madness (Men’s Basketball Division I) was considered. In the end, the predicting bracket was used to compare with the real tournament result, and the model performance was evaluated by bracket scoring system (Shen et al., 2015).
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Xing, Guan. "LASSOING MIXTURES AND BAYESIAN ROBUST ESTIMATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1164135815.
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Braga, Carlos Roberto. "Study of solar-interplanetary-geomagnetic disturbances using data from the Global Muon Detector Network and the LASCO coronagraph." Instituto Nacional de Pesquisas Espaciais, 2011. http://urlib.net/sid.inpe.br/mtc-m19/2011/02.07.20.31.
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O objetivo do trabalho da Dissertação é estudar distúrbios solar-interplanetário-geomagnéticos, como ejeções de massa coronais solares (Coronal Mass Ejections - CMEs) usando observações de coronógrafos de luz branca e de raios cósmicos de alta energia (muons). A partir de imagens do coronógrafo LASCO-C3 (Large Angle and Spectroscopic Coronagraph), ejeções coronais de massa (CMEs) foram segmentadas de forma supervisionada por textura. O contorno identificado foi utilizado para estimar velocidades radiais e de expansão de um conjunto de 57 CMEs associadas a eventos solares próximos ao limbo. Optou-se por segmentação por textura, buscando-se parametrizar estimativas de velocidades de CME que não são consenso. De forma geral o contorno identificado pela técnica mostrou-se coerente com a definição de CME e a posição angular, velocidade radial e de expansão estimadas são similares aos resultados anteriores obtidos por catálogos produzidos manualmente. Por outro lado, usando dados de raios cósmicos de alta energia (muons), assinaturas precedentes a chegada da massa de plasma solar foram estudadas usando dados da Rede Mundial de Detectores de Muons (GMDN). Foi elaborada e estudada a distribuição da intensidade de raios cósmicos como função do ângulo de pitch para períodos associados às 16 tempestades geomagnéticas fracas ou moderadas observadas em 2008. Em 14 dos eventos foram observados possíveis precursores, tanto acréscimos como decréscimos sistemáticos. Não há razão identificada para a ausência de precursores nos dois eventos restantes.The objective of this work is to study solar-interplanetary-geomagnetic disturbances like coronal mass ejections (CMEs) using observations from the white light coronagraph and high-energy cosmic ray (muons). Images from the Large Angle and Spectroscopic Coronagraph (LASCO-C3) were segmented by texture in a supervised way and the identified contour was used to estimate the radial and expansion speed of a set of 57 limb CMEs for the period between 1997 and 2001. Texture analysis was chosen in a way to parameterize the estimation of CMEs contours, which are not always consensus. In a general view, the identified contour is in agreement with the CME definition and the estimate position angle, radial speed and expansion speed are in agreement with previous catalogs manually done. In the other hand, using high-energy cosmic ray (muons) observations, signatures preceding the arrival of plasma structures were studied using data from the Global Muon Detector Network (GMDN). Pitch angle distributions were done for periods associated with the 16 small and moderate geomagnetic storms observed in 2008. Fourteen of them show some possible precursors, both precursory increases and precursory decreases. No clear reason was found yet for not seeing precursors in the remaining two events.
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Linscott, Ross, and Tilo Wiklund. "Parsimonious Dynamical Systems using the LASSO and the Bootstrap." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-233042.
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This project aims to investigate developments of analysis methods for time series panel data proposed by Ranganathan et al.[94]. Model selection is used as a tool for data exploration. We obtain a more stable and consistent model selection by combining stability selection [82] on the adaptive LASSO [125] with some time series bootstrapping methods [88]. The resulting method is also computationally less heavy, allowing it to handle higher dimensional and higher order models. Further, a method for validating an estimated dynamic against local polynomial gradient estimates in the data is proposed. The introduced techniques are motivated in terms of related prior research. After this, a simulation study shows that the bootstrapped stability selection is able to identify models for some non-linear diffusion processes. Finally, the model selection method is applied to real world data previously investigated by Ranganathan et al, giving results that do not match theirs. Implications and possible extensions are discussed. All the implemented procedures are available in packages for the R programming languages, such that one could easily continue investigating either of the introduced methods.
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Jutras, Melanie A. "Dimension Reduction and LASSO using Pointwise and Group Norms." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1254.
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Principal Components Analysis (PCA) is a statistical procedure commonly used for the purpose of analyzing high dimensional data. It is often used for dimensionality reduction, which is accomplished by determining orthogonal components that contribute most to the underlying variance of the data. While PCA is widely used for identifying patterns and capturing variability of data in lower dimensions, it has some known limitations. In particular, PCA represents its results as linear combinations of data attributes. PCA is therefore, often seen as difficult to interpret and because of the underlying optimization problem that is being solved it is not robust to outliers. In this thesis, we examine extensions to PCA that address these limitations. Specific techniques researched in this thesis include variations of Robust and Sparse PCA as well as novel combinations of these two methods which result in a structured low-rank approximation that is robust to outliers. Our work is inspired by the well known machine learning methods of Least Absolute Shrinkage and Selection Operator (LASSO) as well as pointwise and group matrix norms. Practical applications including robust and non-linear methods for anomaly detection in Domain Name System network data as well as interpretable feature selection with respect to a website classification problem are discussed along with implementation details and techniques for analysis of regularization parameters.
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Kunz, Matthew Ross. "Fused Lasso and Tensor Covariance Learning with Robust Estimation." Thesis, The Florida State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10973227.
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With the increase in computation and data storage, there has been a vast collection of information gained with scientific measurement devices. However, with this increase in data and variety of domain applications, statistical methodology must be tailored to specific problems. This dissertation is focused on analyzing chemical information with an underlying structure.
Robust fused lasso leverages information about the neighboring regression coefficient structure to create blocks of coefficients. Robust modifications are made to the mean to account for gross outliers in the data. This method is applied to near infrared spectral measurements in prediction of an aqueous analyte concentration and is shown to improve prediction accuracy.
Expansion on the robust estimation and structure analysis is performed by examining graph structures within a clustered tensor. The tensor is subjected to wavelet smoothing and robust sparse precision matrix estimation for a detailed look into the covariance structure. This methodology is applied to catalytic kinetics data where the graph structure estimates the elementary steps within the reaction mechanism.
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Liu, Tuo. "Model Selection and Adaptive Lasso Estimation of Spatial Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500379101560737.
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Yousef, Mohammed A. "Two-Stage SCAD Lasso for Linear Mixed Model Selection." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.
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Joo, LiJin. "Bayesian lasso| An extension for genome-wide association study." Thesis, New York University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10243856.
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In genome-wide association study (GWAS), variable selection has been used for prioritizing candidate single-nucleotide polymorphism (SNP). Relating densely located SNPs to a complex trait, we need a method that is robust under various genetic architectures, yet is sensitive enough to detect the marginal difference between null and non-null factors. For this problem, ordinary Lasso produced too many false positives, and Bayesian Lasso by Gibbs samplers became too conservative when selection criterion was posterior credible sets. My proposals to improve Bayesian Lasso include two aspects: To use stochastic approximation, variational Bayes for increasing computational efficiency and to use a Dirichlet-Laplace prior for separating small effects from nulls better. Both a double exponential prior of Bayesian Lasso and a Dirichlet-Laplace prior have a global-local mixture representation, and variational Bayes can effectively handle the hierarchies of a model due to the mixture representation. In the analysis of simulated and real sequencing data, the proposed methods showed meaningful improvements on both efficiency and accuracy.
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Mevaere, Jimmy. "Lasso peptides from Actinobacteria - Chemical diversity and ecological role." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066617/document.
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Les peptides lasso sont des peptides bioactifs bactériens issus de la voie de biosynthèse ribosomale et subissant des modifications post-traductionnelles, caractérisés par une structure entrelacée dite en lasso. Ils possèdent un cycle macrolactame en position N-terminale, traversé par la queue C-terminale. Cette topologie de type rotaxane, maintenue par piégeage de la queue C-terminale dans le cycle via des acides aminés encombrant et/ou des ponts disulfure, confère à ces peptides une structure compacte et stable. Les actinobactéries recèlent la plus grande diversité et gamme d'activités biologiques parmi les peptides lasso (antibactériens, anti-VIH, antagonistes de récepteurs..), et l'exploration de génomes suggère une diversité encore plus grande, puisque certains clusters portent des gènes codant des enzymes de modifications post-traductionnelles jamais observées auparavant. Cependant, l'expression de ces peptides semble être rigoureusement contrôlée, rendant leur production en laboratoire difficile à partir de la bactérie productrice. Le rôle écologique et les mécanismes de régulation des peptides lasso ne sont pas très documentés. Leur compréhension permettrait d'améliorer la production et de mieux exploiter les activités biologiques des peptides lassoLasso peptides are ribosomally synthesized and post-translationally modified peptides produced by bacteria, characterized by a remarkable mechanically-interlocked structure. The lasso topology, reminiscent to a rotaxane, consists in an N-terminal macrolactam ring threaded by a C-terminal tail. This compact and stable structure is stabilized by steric entrapping of the tail in the ring, through bulky amino acid(s) and/or disulphide bonds. Lasso peptides produced by Actinobacteria display the greatest chemical diversity and a range of biological activities (antibacterial, anti-HIV, receptor antagonist…), therefore are of high pharmaceutical interest. Genome mining revealed that Actinobacteria have enormous potential to biosynthesize novel lasso peptides, e.g. harbouring new post-translational modifications. However, the expression of these peptides is generally controlled by complex regulatory systems, making their production under laboratory conditions difficult. Understanding the ecological role and regulation mechanisms of lasso peptides would help to improve production and better exploit the biotechnological potential of these molecules. The first part of my work deals with the identification of new lasso peptides from Actinobacteria, using heterologous expression in Streptomyces hosts. The second part of my work deals with the regulation mechanism and ecological role of lasso peptides using sviceucin, a lasso peptide produced by Streptomyces sviceus, as the model for study
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Martín, Gómez Helena. "Development of synthetic strategies for lasso peptides with anticancer activity." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/550974.
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Nowadays, the discovery and development of novel constrained peptides which are likely to combine the advantages of therapeutic proteins with those of small molecules is of special interest. This has partially prompted the re-emergence of peptides as therapeutics. Thus, potentially, these peptides provide both the selectivity and potency of larger protein biologics but with zero or low immunogenicity, and the stability and bioavailability of small molecules. Furthermore, they are smaller than biologics, more accessible and cheaper to manufacture using chemical methods, thus presumably combining the advantages of the two therapeutic approaches.
Lasso peptides are a class of ribosomally synthesized and post-translationally modified natural products with a unique three-dimensional structure, in which the C-terminus threads through an N-terminal macrolactam ring in a right-handed conformation. These peptides consist of 15–26 proteinogenic amino acids and share an N-terminal 7- to 9- residue macrolactam ring where the N-terminal amino acid is always glycine or cysteine and the amino acid that closes the ring is aspartic or glutamic acid. The lasso topology is predominantly stabilized by steric interactions, in the case of class II lasso peptides, but sometimes is assisted by the presence of disulfide bridges; two in the case of class I or one in class III lasso peptides. Currently, a total of 43 lasso peptides have been described; 3 belong to class I, 39 to class II and 1 to class III.1 Prior to 2008, most of these lasso peptides were discovered by isolation from bacteria; however, capistruin, the first lasso peptide isolated by a genome mining approach, changed this scenario.2
The diverse functionality of lasso peptides makes these molecules attractive candidates for drug discovery. In addition, given their extraordinary stability against chemical, thermal and proteolytic degradation1 and reduced flexibility, these peptides are suitable scaffolds for drug design and epitope grafting approaches.3,4 Considering this, it is possible to use a rational approach to further improve and optimize such a scaffold toward the generation of more potent and more selective bioactive compounds.
Currently, all research into new peptide drugs pursues two main common objectives: development of new compounds resistant to enzymatic degradation and the modulation of peptide topology, since the properties are highly related to the shape.5 In this regard, most lasso peptide synthetic strategies are based on the imitation of the interlocked structure of rotaxanes and catenanes.6,7,8,9 Furthermore, lasso peptide-like bicyclic peptides is also a suitable chemical approach, in which the loop sequence is tied with a covalent bond.10
Sungsanpin is a class II lasso peptide isolated from a Streptomyces sp. strain collected in Korea in 2012.11 It shows an inhibitory effect on the invasion of human non-small cell lung cancer (NSCLC), an effect that has been reported with the A549 cell line. Regarding the previously mentioned, the aim of this project is the synthesis of sungsanpin and analogs with linkages able to maintain the threaded lasso structure.
Several characterization techniques have been established for lasso peptides identification. A representative and recent technique that allows rapid structural detection and dynamical features is ion-mobility mass spectrometry (IM-MS). It is a complementary approach to MS/MS experiments that provides information on the global shape of molecules,12 and has proven useful for the structural characterization of many lasso peptides.13,14
To date, no synthetic access to lasso peptides is available due to the difficulty in building and maintaining the threaded lasso structure. The ability to generate lasso peptides synthetically remains a challenging endeavor and it would open the door to the production of lasso peptide analog with unnatural amino acids or other nonproteinogenic building blocks. From a therapeutic point of view, these small and constrained structures would represent a new paradigm in drug discovery.
(1) Hegemann, J. D.; Zimmermann, M.; Xie, X.; Marahiel, M. A. Acc. Chem. Res. 2015, 48
(7), 1909.
(2) Knappe, T. a.; Linne, U.; Zirah, S.; Rebuffat, S.; Xie, X.; Marahiel, M. a. J. Am. Chem. Soc. 2008, 130 (17), 11446.
(3) Knappe, T. A.; Manzenrieder, F.; Mas-Moruno, C.; Linne, U.; Sasse, F.; Kessler, H.; Xie, X.; Marahiel, M. A. Angew. Chemie - Int. Ed. 2011, 50 (37), 8714.
(4) Hegemann, J. D.; De Simone, M.; Zimmermann, M.; Knappe, T. A.; Xie, X.; Di Leva, F. S.; Marinelli, L.; Novellino, E.; Zahler, S.; Kessler, H.; Marahiel, M. A. J. Med. Chem. 2014, 57 (13), 5829.
(5) Clavel, C.; Fournel-Marotte, K.; Coutrot, F. Molecules 2013, 18 (9), 11553.
(6) Mohr, B.; Weck, M.; Sauvage, J.-P.; Grubbs, R. H. Angew. Chem. Int. Ed. Engl. 1997,
36 (12), 1308.
(7) Hogg, L.; Leigh, D. A.; Lusby, P. J.; Morelli, A.; Parsons, S.; Wong, J. K. Y. Angew. Chemie - Int. Ed. 2004, 43 (10), 1218.
(8) Hänni, K. D.; Leigh, D. A. Chem. Soc. Rev. 2010, 39 (4), 1240.
(9) Yan, L. Z.; Dawson, P. E. Angew. Chemie Int. Ed. 2001, 40 (19), 3625.
(10) Soudy, R.; Wang, L.; Kaur, K. Bioorganic Med. Chem. 2012, 20 (5), 1794.
(11) Um, S.; Kim, Y.-J. J.; Kwon, H. H. C.; Wen, H.; Kim, S.-H. H.; Kwon, H. H. C.; Park, S.; Shin, J.; Oh, D.-C. C. J. Nat. Prod. 2013, 76 (5), 873.
(12) Clemmer, D. E.; Jarrold, M. F. J. Mass Spectrom. 1997, 32 (6), 577.
(13) Jeanne Dit Fouque, K.; Afonso, C.; Zirah, S.; Hegemann, J. D.; Zimmermann, M.; Marahiel, M. A.; Rebuffat, S.; Lavanant, H. Anal. Chem. 2015, 87 (2), 1166.
(14) Fouque, K. J. D.; Lavanant, H.; Zirah, S.; Hegemann, J. D.; Zimmermann, M.; Marahiel, M. A.; Rebuffat, S.; Afonso, C. J. Am. Soc. Mass Spectrom. 2016.Els pèptids llaç són una classe de productes naturals sintetitzats al ribosoma i modificats després de la translació amb una estructura tridimensional única, en la qual el C-terminal travessa l’anell de macrolactama N-terminal en una conformació de mà dreta. Aquests pèptids consisteixen en 15-26 aminoàcids proteïnògens i comparteixen un anell de macrolactama N-terminal de 7 - 9 residus on l'aminoàcid N- terminal és sempre glicina o cisteïna i l'aminoàcid que tanca l'anell és àcid aspàrtic o glutàmic. La topologia de llaç està predominantment estabilitzada per interaccions estèriques, en el cas dels pèptids de la classe II, però de vegades és assistida per la presència de ponts disulfurs; dos en el cas dels pèptids de la classe I o un, en la classe III. Tenint en compte la diversa funcionalitat dels pèptids llaç i la seva extraordinària estabilitat davant la degradació química, tèrmica i proteolítica, aquests pèptids són bastiments proteínics adequats pel disseny de fàrmacs i les aproximacions d’empelt d’epítops.1,2 Tenint en compte això, és possible utilitzar un enfocament racional per millorar i optimitzar encara més aquests bastiments proteínics cap a la generació de compostos bioactius més potents i selectius. Sungsanpin, és un pèptid llaç de classe II, aïllat d'una soca de Streptomyces sp. recollida a Korea al 2012.3 Sungsanpin mostra un efecte inhibitori sobre la invasió del càncer de pulmó humà no microcític (NSCLC), un efecte que s'ha estudiat amb la línia cel·lular A549. L'objectiu d'aquest projecte és la síntesi del sungsanpin i anàlegs amb enllaços capaços de mantenir l'estructura roscada de llaç. Tanmateix, fins avui, no hi ha accés sintètic als pèptids llaç a causa de la dificultat a l'hora de construir i mantenir l'estructura roscada. La capacitat de generar aquests tipus de pèptids sintèticament continua sent un repte desafiant i obriria la porta a la producció d’anàlegs de pèptids llaç amb aminoàcids no naturals o altres blocs de construcció no proteïnògens. Des d'un punt de vista terapèutic, aquestes estructures petites i restringides representarien un nou paradigma en el descobriment de fàrmacs. (1) Knappe, T. A.; Manzenrieder, F.; Mas-Moruno, C.; Linne, U.; Sasse, F.; Kessler, H.; Xie, X.; Marahiel, M. A. Angew. Chemie - Int. Ed. 2011, 50 (37), 8714. (2) Hegemann, J. D.; De Simone, M.; Zimmermann, M.; Knappe, T. A.; Xie, X.; Di Leva, F. S.; Marinelli, L.; Novellino, E.; Zahler, S.; Kessler, H.; Marahiel, M. A. J. Med. Chem. 2014, 57 (13), 5829. (3) Um, S.; Kim, Y.-J. J.; Kwon, H. H. C.; Wen, H.; Kim, S.-H. H.; Kwon, H. H. C.; Park, S.; Shin, J.; Oh, D.-C. C. J. Nat. Prod. 2013, 76 (5), 873.
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Caster, Ola. "Mining the WHO Drug Safety Database Using Lasso Logistic Regression." Thesis, Uppsala University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120981.
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Hebiri, Mohamed. "Quelques questions de sélection de variables autour de l'estimateur LASSO." Phd thesis, Université Paris-Diderot - Paris VII, 2009. http://tel.archives-ouvertes.fr/tel-00408737.
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Le problème général étudié dans cette thèse est celui de la régression linéaire en grande dimension. On s'intéresse particulièrement aux méthodes d'estimation qui capturent la sparsité du paramètre cible, même dans le cas où la dimension est supérieure au nombre d'observations. Une méthode populaire pour estimer le paramètre inconnu de la régression dans ce contexte est l'estimateur des moindres carrés pénalisés par la norme ℓ1 des coefficients, connu sous le nom de LASSO. Les contributions de la thèse portent sur l'étude de variantes de l'estimateur LASSO pour prendre en compte soit des informations supplémentaires sur les variables d'entrée, soit des modes semi-supervisés d'acquisition des données. Plus précisément, les questions abordées dans ce travail sont : i) l'estimation du paramètre inconnu lorsque l'espace des variables explicatives a une structure bien déterminée (présence de corrélations, structure d'ordre sur les variables ou regroupements entre variables) ; ii) la construction d'estimateurs adaptés au cadre transductif, pour lequel les nouvelles observations non étiquetées sont prises en considération. Ces adaptations sont en partie déduites par une modification de la pénalité dans la définition de l'estimateur LASSO. Les procédures introduites sont essentiellement analysées d'un point de vue non-asymptotique ; nous prouvons notamment que les estimateurs construits vérifient des Inégalités de Sparsité Oracles. Ces inégalités ont pour particularité de dépendre du nombre de composantes non-nulles du paramètre cible. Un contrôle sur la probabilité d'erreur d'estimation du support du paramètre de régression est également établi. Les performances pratiques des méthodes étudiées sont par ailleurs illustrées à travers des résultats de simulation.
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Chen, Xiaohui. "Lasso-type sparse regression and high-dimensional Gaussian graphical models." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42271.
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High-dimensional datasets, where the number of measured variables is larger than the sample size, are not uncommon in modern real-world applications such as functional Magnetic Resonance Imaging (fMRI) data. Conventional statistical signal processing tools and mathematical models could fail at handling those datasets. Therefore, developing statistically valid models and computationally efficient algorithms for high-dimensional situations are of great importance in tackling practical and scientific problems. This thesis mainly focuses on the following two issues: (1) recovery of sparse regression coefficients in linear systems; (2) estimation of high-dimensional covariance matrix and its inverse matrix, both subject to additional random noise.
In the first part, we focus on the Lasso-type sparse linear regression. We propose two improved versions of the Lasso estimator when the signal-to-noise ratio is low: (i) to leverage adaptive robust loss functions; (ii) to adopt a fully Bayesian modeling framework. In solution (i), we propose a robust Lasso with convex combined loss function and study its asymptotic behaviors. We further extend the asymptotic analysis to the Huberized Lasso, which is shown to be consistent even if the noise distribution is Cauchy. In solution (ii), we propose a fully Bayesian Lasso by unifying discrete prior on model size and continuous prior on regression coefficients in a single modeling framework. Since the proposed Bayesian Lasso has variable model sizes, we propose a reversible-jump MCMC algorithm to obtain its numeric estimates.
In the second part, we focus on the estimation of large covariance and precision matrices. In high-dimensional situations, the sample covariance is an inconsistent estimator. To address this concern, regularized estimation is needed. For the covariance matrix estimation, we propose a shrinkage-to-tapering estimator and show that it has attractive theoretic properties for estimating general and large covariance matrices. For the precision matrix estimation, we propose a computationally efficient algorithm that is based on the thresholding operator and Neumann series expansion. We prove that, the proposed estimator is consistent in several senses under the spectral norm. Moreover, we show that the proposed estimator is minimax in a class of precision matrices that are approximately inversely closed.
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Konzen, Evandro. "Penalizações tipo lasso na seleção de covariáveis em séries temporais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/103896.
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Este trabalho aplica algumas formas de penalização tipo LASSO aos coeficientes para reduzir a dimensionalidade do espaço paramétrico em séries temporais, no intuito de melhorar as previsões fora da amostra. Particularmente, o método denominado aqui como WLadaLASSO atribui diferentes pesos para cada coeficiente e para cada defasagem. Nas implementações de Monte Carlo deste trabalho, quando comparado a outros métodos de encolhimento do conjunto de coeficientes, essencialmente nos casos de pequenas amostras, o WLadaLASSO mostra superioridade na seleção das covariáveis, na estimação dos parâmetros e nas previsões. Uma aplicação a séries macroeconômicas brasileiras também mostra que tal abordagem apresenta a melhor performance de previsão do PIB brasileiro comparada a outras abordagens.This dissertation applies some forms of LASSO-type penalty on the coefficients to reduce the dimensionality of the parameter space in time series, in order to improve the out-of-sample forecasting. Particularly, the method named here as WLadaLASSO assigns different weights to each coefficient and lag period. In Monte Carlo implementations in this study, when compared to other shrinkage methods, essentially for small samples, the WLadaLASSO shows superiority in the covariable selection, in the parameter estimation and in forecasting. An application to Brazilian macroeconomic series also shows that this approach has the best forecasting performance of the Brazilian GDP compared to other approaches.
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Tang, Shuhan. "Spectral Analysis Using Multitaper Whittle Methods with a Lasso Penalty." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586863604571678.
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Pan, Juming. "Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921.
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Vachon, Thomas. "Christopher Lasch : de la critique du progrès au libéralisme tragique." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39658.
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Christopher Lasch (1932-1994) est un historien et un critique social américain connu pour ses
ouvrages portant sur la politique et la culture américaines au XXème siècle. Cette thèse cherche à
éclairer sa présentation de l’idée de progrès et de ses conséquences pour la polis américaine. Nous
interprétons la pensée de Lasch comme un « libéralisme tragique », en voulant signifier par-là que
l’historien américain voit une tension non résolue entre le libéralisme politique et l’idée de progrès.
Pour présenter ce constat, nous abordons tout d’abord la genèse de cette formulation critique de
l’idée de progrès en présentant une succincte biographie intellectuelle de Lasch. Nous examinons,
ensuite, l’histoire que Lasch fait de l’idée de progrès dans son maître ouvrage de 1991 intitulé, The
True and Only Heaven: Progress and Its Critics. Nous analysons enfin les conséquences sur la vie
politique et sociale des démocraties libérales contemporaines que Lasch analyse à la suite de ce
qu’il juge être l’expansion et le triomphe sans partage de l’idée de progrès dans nos sociétés. Ce
dernier point nous permettra d’illustrer en quoi la conception laschienne du libéralisme est, à notre
avis, tragique.
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Chiesurin, Marilisa <1981>. "Intervalli di previsione ottenuti dal LASSO: uno studio di simulazione." Master's Degree Thesis, Università Ca' Foscari Venezia, 2013. http://hdl.handle.net/10579/2532.
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He, Shiquan. "A Review of Linear Regression and some Basic Proofs for Lasso." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/88.
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The goal of this paper is to do some basic proofs for lasso and have a deep understanding of linear regression. In this paper, firstly I give a review of methods in linear regression, and most concerns with the method of lasso. Lasso for ¡®least absolute shrinkage and selection operator¡¯ is a regularized version of method adds a constraint which uses norm less or equal to a given value t. By doing so, some predictor coefficients would be shrank and some others might be set to 0. We can attain good interpretation and prediction accuracy by using lasso method. Secondly, I provide some basic proofs for lasso, which would be very helpful in understanding lasso. Additionally, some geometric graphs are also given and one example is illustrated.
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Fransson, Viktor. "Graphical lasso for covariance structure learning in the high dimensional setting." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-176485.
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This thesis considers the estimation of undirected Gaussian graphical models especially in the high dimensional setting where the true observations are assumed to be non-Gaussian distributed. The first aim is to present and compare the performances of existing Gaussian graphical model estimation methods. Furthermore since the models rely heavily on the normality assumption, various methods for relaxing the normal assumption are presented. In addition to the existing methods, a modified version of the joint graphical lasso method is introduced which monetizes on the strengths of the community Bayes method. The community Bayes method is used to partition the features (or variables) of datasets consisting of several classes into several communities which are estimated to be mutually independent within each class which allows the calculations when performing the joint graphical lasso method, to be split into several smaller parts. The method is also inspired by the cluster graphical lasso and is applicable to both Gaussian and non-Gaussian data, assuming that the normal assumption is relaxed. Results show that the introduced cluster joint graphical lasso method outperforms com-peting methods, producing graphical models which are easier to comprehend due to the added information obtained from the clustering step of the method. The cluster joint graphical lasso is applied to a real dataset consisting of p = 12582 features which resulted in computation gain of a factor 35 when comparing to the competing method which is very significant when analysing large datasets. The method also allows for parallelization where computations can be spread across several computers greatly increasing the computational efficiency.Denna rapport behandlar uppskattningen av oriktade Gaussiska grafiska modeller speciellt i högdimensionell miljö där dom verkliga observationerna antas vara icke-Gaussiska fördelade. Det första målet är att presentera och jämföra prestandan av befintliga metoder för uppskattning av Gaussiska grafiska modeller. Eftersom modellerna är starkt beroende av normalantagandet, så kommer flertalet metoder för att relaxa normalantagandet att presenteras. Utöver dom befintliga metoderna, kommer en modifierad version av joint graphical lasso att introduceras som bygger på styrkan av community Bayes metod. Community Bayes metod används för att partitionera variabler från datamängder som består av flera klasser i flera samhällen (eller communities) som antas vara oberoende av varandra i varje klass. Detta innebär att beräkningarna av joint graphical lasso kan delas upp i flera mindre problem. Metoden är också inspirerad av cluster graphical lasso och applicerbar för både Gaussisk och icke-gaussisk data, förutsatt att det normala antagandet är relaxed. Resultaten visar att den introducerade cluster joint graphical lasso metoden utklassar konkurrerande metoder, som producerar grafiska modeller som är lättare att förstå på grund av den extra information som erhålls från klustringssteget av metoden. Joint graphical lasso appliceras även på en verklig datauppsättning bestående av p = 12582 variabler som resulterade i minskad beräkningstid av en faktor 35 vid jämförelse av konkurrerande metoder. Detta är mycket betydande när man analyserar stora datamängder. Metoden möjliggör också parallellisering där beräkningar kan spridas över flera datorer vilket ytterligare kraftigt ökar beräkningseffektiviteten.
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Younkin, Samuel G. "The Linkage Disequilibrium LASSO for SNP Selection in Genetic Association Studies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1291219489.
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Zhou, Xiaofei. "Bayesian Lasso for Detecting Rare Genetic Variants Associated with Common Diseases." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563455460578675.
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Ruan, Lingyan. "Statistical analysis of high dimensional data." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37135.
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This century is surely the century of data (Donoho, 2000). Data analysis has been an emerging activity over the last few decades. High dimensional data is in particular more and more pervasive with the advance of massive data collection system, such as microarrays, satellite imagery, and financial data. However, analysis of high dimensional data is of challenge with the so called curse of dimensionality (Bellman 1961). This research dissertation presents several methodologies in the application of high dimensional data analysis.
The first part discusses a joint analysis of multiple microarray gene expressions. Microarray analysis dates back to Golub et al. (1999). It draws much attention after that. One common goal of microarray analysis is to determine which genes are differentially expressed. These genes behave significantly differently between groups of individuals. However, in microarray analysis, there are thousands of genes but few arrays (samples, individuals) and thus relatively low reproducibility remains. It is natural to consider joint analyses that could combine microarrays from different experiments effectively in order to achieve improved accuracy. In particular, we present a model-based approach for better identification of differentially expressed genes by incorporating data from different studies. The model can accommodate in a seamless fashion a wide range of studies including those performed at different platforms, and/or under different but overlapping biological conditions. Model-based inferences can be done in an empirical Bayes fashion. Because of the information sharing among studies, the joint analysis dramatically improves inferences based on individual analysis. Simulation studies and real data examples are presented to demonstrate the effectiveness of the proposed approach under a variety of complications that often arise in practice.
The second part is about covariance matrix estimation in high dimensional data. First, we propose a penalised likelihood estimator for high dimensional t-distribution. The student t-distribution is of increasing interest in mathematical finance, education and many other applications. However, the application in t-distribution is limited by the difficulty in the parameter estimation of the covariance matrix for high dimensional data. We show that by imposing LASSO penalty on the Cholesky factors of the covariance matrix, EM algorithm can efficiently compute the estimator and it performs much better than other popular estimators.
Secondly, we propose an estimator for high dimensional Gaussian mixture models. Finite Gaussian mixture models are widely used in statistics thanks to its great flexibility. However, parameter estimation for Gaussian mixture models with high dimensionality can be rather challenging because of the huge number of parameters that need to be estimated. For such purposes, we propose a penalized likelihood estimator to specifically address such difficulties. The LASSO penalty we impose on the inverse covariance matrices encourages sparsity on its entries and therefore helps reducing the dimensionality of the problem. We show that the proposed estimator can be efficiently computed via an Expectation-Maximization algorithm. To illustrate the practical merits of the proposed method, we consider its application in model-based clustering and mixture discriminant analysis. Numerical experiments with both simulated and real data show that the new method is a valuable tool in handling high dimensional data.
Finally, we present structured estimators for high dimensional Gaussian mixture models. The graphical representation of every cluster in Gaussian mixture models may have the same or similar structure, which is an important feature in many applications, such as image processing, speech recognition and gene network analysis. Failure to consider the sharing structure would deteriorate the estimation accuracy. To address such issues, we propose two structured estimators, hierarchical Lasso estimator and group Lasso estimator. An EM algorithm can be applied to conveniently solve the estimation problem. We show that when clusters share similar structures, the proposed estimator perform much better than the separate Lasso estimator.
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Lasso, de la Vega Gabriel Pullés-Linares Nidia. "De Cortés valeroso, y Mexicana /." Frankfurt am Main : Madrid : Vervuert ; Iberoamericana, 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014577580&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Al-Kenani, Ali J. Kadhim. "Some statistical methods for dimension reduction." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7727.
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The aim of the work in this thesis is to carry out dimension reduction (DR) for high dimensional (HD) data by using statistical methods for variable selection, feature extraction and a combination of the two. In Chapter 2, the DR is carried out through robust feature extraction. Robust canonical correlation (RCCA) methods have been proposed. In the correlation matrix of canonical correlation analysis (CCA), we suggest that the Pearson correlation should be substituted by robust correlation measures in order to obtain robust correlation matrices. These matrices have been employed for producing RCCA. Moreover, the classical covariance matrix has been substituted by robust estimators for multivariate location and dispersion in order to get RCCA. In Chapter 3 and 4, the DR is carried out by combining the ideas of variable selection using regularisation methods with feature extraction, through the minimum average variance estimator (MAVE) and single index quantile regression (SIQ) methods, respectively. In particular, we extend the sparse MAVE (SMAVE) reported in (Wang and Yin, 2008) by combining the MAVE loss function with different regularisation penalties in Chapter 3. An extension of the SIQ of Wu et al. (2010) by considering different regularisation penalties is proposed in Chapter 4. In Chapter 5, the DR is done through variable selection under Bayesian framework. A flexible Bayesian framework for regularisation in quantile regression (QR) model has been proposed. This work is different from Bayesian Lasso quantile regression (BLQR), employing the asymmetric Laplace error distribution (ALD). The error distribution is assumed to be an infinite mixture of Gaussian (IMG) densities.
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Pelawa, Watagoda Lasanthi Chathurika Ranasinghe. "INFERENCE AFTER VARIABLE SELECTION." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1424.
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This thesis presents inference for the multiple linear regression model Y = beta_1 x_1 + ... + beta_p x_p + e after model or variable selection, including prediction intervals for a future value of the response variable Y_f, and testing hypotheses with the bootstrap. If n is the sample size, most results are for n/p large, but prediction intervals are developed that may increase in average length slowly as p increases for fixed n if the model is sparse: k predictors have nonzero coefficients beta_i where n/k is large.
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Fisher, Gary. "The Munich Kapelle of Orlando di Lasso, 1563-1594 : a model for Renaissance choral performance practice /." Full-text version available from OU Domain via ProQuest Digital Dissertations, 1987.
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Breheny, Patrick John. "Regularized methods for high-dimensional and bi-level variable selection." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/325.
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Many traditional approaches cease to be useful when the number of variables is large in comparison with the sample size. Penalized regression methods have proved to be an attractive approach, both theoretically and empirically, for dealing with these problems. This thesis focuses on the development of penalized regression methods for high-dimensional variable selection. The first part of this thesis deals with problems in which the covariates possess a grouping structure that can be incorporated into the analysis to select important groups as well as important members of those groups. I introduce a framework for grouped penalization that encompasses the previously proposed group lasso and group bridge methods, sheds light on the behavior of grouped penalties, and motivates the proposal of a new method, group MCP.
The second part of this thesis develops fast algorithms for fitting models with complicated penalty functions such as grouped penalization methods. These algorithms combine the idea of local approximation of penalty functions with recent research into coordinate descent algorithms to produce highly efficient numerical methods for fitting models with complicated penalties. Importantly, I show these algorithms to be both stable and linear in the dimension of the feature space, allowing them to be efficiently scaled up to very large problems.
In the third part of this thesis, I extend the idea of false discovery rates to penalized regression. The Karush-Kuhn-Tucker conditions describing penalized regression estimates provide testable hypotheses involving partial residuals. I use these hypotheses to connect the previously disparate elds of multiple comparisons and penalized regression, develop estimators for the false discovery rates of methods such as the lasso and elastic net, and establish theoretical results.
Finally, the methods from all three sections are studied in a number of simulations and applied to real data from gene expression and genetic association studies.