Global ETD Search

1	Applied Adaptive Optimal Design and Novel Optimization Algorithms for Practical Use Strömberg, Eric January 2016 (has links) The costs of developing new pharmaceuticals have increased dramatically during the past decades. Contributing to these increased expenses are the increasingly extensive and more complex clinical trials required to generate sufficient evidence regarding the safety and efficacy of the drugs. It is therefore of great importance to improve the effectiveness of the clinical phases by increasing the information gained throughout the process so the correct decision may be made as early as possible. Optimal Design (OD) methodology using the Fisher Information Matrix (FIM) based on Nonlinear Mixed Effect Models (NLMEM) has been proven to serve as a useful tool for making more informed decisions throughout the clinical investigation. The calculation of the FIM for NLMEM does however lack an analytic solution and is commonly approximated by linearization of the NLMEM. Furthermore, two structural assumptions of the FIM is available; a full FIM and a block-diagonal FIM which assumes that the fixed effects are independent of the random effects in the NLMEM. Once the FIM has been derived, it can be transformed into a scalar optimality criterion for comparing designs. The optimality criterion may be considered local, if the criterion is based on singe point values of the parameters or global (robust), where the criterion is formed for a prior distribution of the parameters. Regardless of design criterion, FIM approximation or structural assumption, the design will be based on the prior information regarding the model and parameters, and is thus sensitive to misspecification in the design stage. Model based adaptive optimal design (MBAOD) has however been shown to be less sensitive to misspecification in the design stage. The aim of this thesis is to further the understanding and practicality when performing standard and MBAOD. This is to be achieved by: (i) investigating how two common FIM approximations and the structural assumptions may affect the optimized design, (ii) reducing runtimes complex design optimization by implementing a low level parallelization of the FIM calculation, (iii) further develop and demonstrate a framework for performing MBAOD, (vi) and investigate the potential advantages of using a global optimality criterion in the already robust MBAOD. Nonlinear Mixed Effects Models Pharmacometrics Fisher Information Matrix Approximation Optimality Criterion Parallelization Model Based Adaptive Optimal Design
2	Planification et analyse de données spatio-temporelles / Design and analysis of spatio-temporal data Faye, Papa Abdoulaye 08 December 2015 (has links) La Modélisation spatio-temporelle permet la prédiction d’une variable régionalisée à des sites non observés du domaine d’étude, basée sur l’observation de cette variable en quelques sites du domaine à différents temps t donnés. Dans cette thèse, l’approche que nous avons proposé consiste à coupler des modèles numériques et statistiques. En effet en privilégiant l’approche bayésienne nous avons combiné les différentes sources d’information : l’information spatiale apportée par les observations, l’information temporelle apportée par la boîte noire ainsi que l’information a priori connue du phénomène. Ce qui permet une meilleure prédiction et une bonne quantification de l’incertitude sur la prédiction. Nous avons aussi proposé un nouveau critère d’optimalité de plans d’expérience incorporant d’une part le contrôle de l’incertitude en chaque point du domaine et d’autre part la valeur espérée du phénomène. / Spatio-temporal modeling allows to make the prediction of a regionalized variable at unobserved points of a given field, based on the observations of this variable at some points of field at different times. In this thesis, we proposed a approach which combine numerical and statistical models. Indeed by using the Bayesian methods we combined the different sources of information : spatial information provided by the observations, temporal information provided by the black-box and the prior information on the phenomenon of interest. This approach allowed us to have a good prediction of the variable of interest and a good quantification of incertitude on this prediction. We also proposed a new method to construct experimental design by establishing a optimality criterion based on the uncertainty and the expected value of the phenomenon. Prédiction spatio-temporelle Bayésien Information spatiale Information temporelle Boîte noire A priori Plans d’expérience Critère d’optimalité Spatio-temporal prediction Bayesian Spatial information Temporal information Black-box Prior information Experimental design Optimality criterion
3	Studies on two specific inverse problems from imaging and finance Rückert, Nadja 20 July 2012 (has links) (PDF) This thesis deals with regularization parameter selection methods in the context of Tikhonov-type regularization with Poisson distributed data, in particular the reconstruction of images, as well as with the identiﬁcation of the volatility surface from observed option prices. In Part I we examine the choice of the regularization parameter when reconstructing an image, which is disturbed by Poisson noise, with Tikhonov-type regularization. This type of regularization is a generalization of the classical Tikhonov regularization in the Banach space setting and often called variational regularization. After a general consideration of Tikhonov-type regularization for data corrupted by Poisson noise, we examine the methods for choosing the regularization parameter numerically on the basis of two test images and real PET data. In Part II we consider the estimation of the volatility function from observed call option prices with the explicit formula which has been derived by Dupire using the Black-Scholes partial diﬀerential equation. The option prices are only available as discrete noisy observations so that the main diﬃculty is the ill-posedness of the numerical diﬀerentiation. Finite diﬀerence schemes, as regularization by discretization of the inverse and ill-posed problem, do not overcome these diﬃculties when they are used to evaluate the partial derivatives. Therefore we construct an alternative algorithm based on the weak formulation of the dual Black-Scholes partial diﬀerential equation and evaluate the performance of the ﬁnite diﬀerence schemes and the new algorithm for synthetic and real option prices. Tikhonov-Regularisierung Kullback-Leibler Totale Variation Poissonverteilte Daten Bildverarbeitung Wahl des Regularisierungsparameters Diskrepanzprinzip L-Kurve Quasi-Optimalitätskriterium PET Black-Scholes-Modell Volatilität Dupire Call-Option Regularisierung durch Diskretisierung Parameter Identifikationsalgorithmus Tikhonov-Regularization Kullback-Leibler Total Variation Poisson distributed data discrepancy principle L-curve method quasi-optimality criterion PET Black-Scholes model volatility Dupire call option regularization by discretization imaging finance ddc:515 ddc:519 Tichonov-Regularisierung Black-Scholes-Modell Volatilität Regularisierungsverfahren Poisson-Verteilung Bildverarbeitung Call-Option Parameter <Mathematik>
4	Studies on two specific inverse problems from imaging and finance Rückert, Nadja 16 July 2012 (has links) This thesis deals with regularization parameter selection methods in the context of Tikhonov-type regularization with Poisson distributed data, in particular the reconstruction of images, as well as with the identiﬁcation of the volatility surface from observed option prices. In Part I we examine the choice of the regularization parameter when reconstructing an image, which is disturbed by Poisson noise, with Tikhonov-type regularization. This type of regularization is a generalization of the classical Tikhonov regularization in the Banach space setting and often called variational regularization. After a general consideration of Tikhonov-type regularization for data corrupted by Poisson noise, we examine the methods for choosing the regularization parameter numerically on the basis of two test images and real PET data. In Part II we consider the estimation of the volatility function from observed call option prices with the explicit formula which has been derived by Dupire using the Black-Scholes partial diﬀerential equation. The option prices are only available as discrete noisy observations so that the main diﬃculty is the ill-posedness of the numerical diﬀerentiation. Finite diﬀerence schemes, as regularization by discretization of the inverse and ill-posed problem, do not overcome these diﬃculties when they are used to evaluate the partial derivatives. Therefore we construct an alternative algorithm based on the weak formulation of the dual Black-Scholes partial diﬀerential equation and evaluate the performance of the ﬁnite diﬀerence schemes and the new algorithm for synthetic and real option prices. info:eu-repo/classification/ddc/515 ddc:515 info:eu-repo/classification/ddc/519 ddc:519

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