<p><font face="TimesNewRomanPSMT"> <p align="left">The objective of the present investigation is to calculate observables using the nuclear shell model for a variety of nuclear phenomena and to make comparisons with experimental data as well as the older interaction. The shell-model code OXBASH was employed for the calculations. Quantities investigated include energy level schemes, static magnetic dipole and electric quadrupole moments, electromagnetic transition probabilities, spectroscopic factors and beta decay rates.</p> </font></p>

<p><font face="TimesNewRomanPSMT"> <p align="left">The objective of the present investigation is to calculate observables using the nuclear shell model for a variety of nuclear phenomena and to make comparisons with experimental data as well as the older interaction. The shell-model code OXBASH was employed for the calculations. Quantities investigated include energy level schemes, static magnetic dipole and electric quadrupole moments, electromagnetic transition probabilities, spectroscopic factors and beta decay rates.</p> </font></p>

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<p>The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prior distributions in an objective manner, providing conditions under which Bayes factors lead to the correct model selection, particularly in the situation where the number of variables, <italic>p</italic>, increases with the sample size, <italic>n</italic>. This is certainly the case in our area of motivation; the biological application of genetic association studies involving single nucleotide polymorphisms. While the most common approach to this problem has been to apply a marginal test to all genetic markers, we employ analytical strategies that improve upon these marginal methods by modeling the outcome variable as a function of a multivariate genetic profile using Bayesian variable selection. In doing so, we perform variable selection on a large number of correlated covariates within studies involving modest sample sizes. </p> <p>In particular, we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally 'validated' in independent studies. </p> <p></p> <p>In the context of Bayesian model uncertainty for problems involving a large number of correlated covariates we characterize commonly used prior distributions on the model space and investigate their implicit multiplicity correction properties first in the extreme case where the model includes an increasing number of redundant covariates and then under the case of full rank design matrices. We provide conditions on the asymptotic (in <italic>n</italic> and <italic>p</italic>) behavior of the model space prior </p> <p>required to achieve consistent selection of the global hypothesis of at least one associated variable in the analysis using global posterior probabilities (i.e. under 0-1 loss). In particular, under the assumption that the null model is true, we show that the commonly used uniform prior on the model space leads to inconsistent selection of the global hypothesis via global posterior probabilities (the posterior probability of at least one association goes to <italic>1</italic>) when the rank of the design matrix is finite. In the full rank case, we also show inconsistency when <italic>p</italic> goes to infinity faster than the square root of <italic>n</italic>. Alternatively, we show that any model space prior such that the global prior odds of association increases at a rate slower than the square root of <italic>n<italic> results in consistent selection of the global hypothesis in terms of posterior probabilities.</p> / Dissertation

This thesis deals with the appropriate handling of spatial data in general, and in particular in the framework of economic sciences. An overview of well known methods from the field of spatial statistics and spatial econometrics is given. Furthermore a special class of spatial objects is described, namely objects that are that far apart from all other observations in the dataset, that they are not connected to them anymore. Different treatments of such objects are suggested and their influence on the Moran's I test for spatial autocorrelation is analyzed in more detail. After this theoretical part some adequate spatial methods are applied to the well-known problem of R&D spillovers. The corresponding dataset is not obviously spatial, nevertheless spatial methods can be used. The spatial contiguity matrix is based on an economic distance measure instead of the commonly used geographic distances. Finally, optimal design theory and spatial analysis are combined via a new criterion. This criterion was developed to be able to take a potential spatial dependency of the data points into account. The aim is to find the best design points that show the same spatial dependence structure as the true population. (author's abstract)

In this paper we estimate space-time impacts of the embeddedness in R&D networks on regional knowledge production by means of a dynamic spatial panel data model with non-linear effects for a set of 229 European NUTS-2 regions in the period 1999-2009. Embeddedness refers to the positioning in networks where nodes represent regions that are linked by joint R&D endeavours in European Framework Programmes. We observe positive immediate impacts on regional knowledge production arising from increased embeddedness in EU funded R&D networks, in particular for regions with lower own knowledge endowments. However, long-term impacts of R&D network embeddedness are comparatively small.(authors' abstract) / Series: Department of Economics Working Paper Series

Cette thèse s'articule autour d'une notion spectrale assez récente, appelée le spectre étendu des opérateurs. Dans la première partie nous fournissons des propriétés générales du spectre étendu d'un opérateur dans certains cas particuliers, tels que le cas de dimension finie et celui des opérateurs inversibles. Nous nous intéressons dans la deuxième partie à l'étude du spectre étendu de l'opérateur shift tronqué Su. En particulier, nous donnons une description complète des vecteurs propres étendus associes à chaque valeur propre étendue de Sb, ou b est un produit de Blaschke quelconque. Dans la troisième partie nous décrirons complètement le spectre étendu et les sous espaces propres étendus d'une classe d'opérateurs très importante : celle des opérateurs normaux. Nous commençons d'abord par la classe des opérateurs qui sont produits d'un opérateur positif par un autoadjoint. Ensuite, nous utilisons le théorème de Fuglede-Putnam pour déduire une description complète des valeurs et des vecteurs propres étendus des opérateurs normaux, en fonction de leur mesure spectrale. Dans la dernière partie, nous appliquons nos résultats des trois premières parties sur des exemples concrets. En particulier, nous traitons= le problème des sous espaces propres étendus des opérateurs définis dans un espace de dimension finie. Ensuite, nous montrons l'existence d'un opérateur compact quasinilpotent dont le spectre étendu est réduit au singleton {1}. Enfin, nous traitons deux opérateurs de Cesaro très importants dans les applications / This thesis is based on a relatively new spectral notion, called extended spectrum of operators. In the first part, we provide general properties of extended spectrum of an operator in some special cases, such as the case of finite dimension and the case of invertible operator. We focused in the second part on characterizing the extended spectrum of truncated shift operator Su. In particular, we give a complete description of the extended eigenvectors associated to each extended eigenvalue of Sb, where b is a Blaschke product. In the third part, we describe the extended spectrum and the extended eigenvectors of a very important class of operators , that is the normal operators. We first start by describing these last sets for the product of a positive and a self-adjoint operator which are both injective. After, we use the Fuglede-Putnam theorem to describe the same sets for normal operators, in terms of their spectral measure. In the last part, we apply our results from the last three parts on concrete examples. In particular, we address the problem of extended eigenvectors of operators defined in a finite dimension space. Next, we show the existence of a quasinilpotent compact operator whose extended spectrum is reduced to {1}. Finally, we study two Cesaro operators which are very important in applications

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