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This paper examines the extent to which thin markets in conjunction with tax induced kinks in the budget constraint cause consumer demand to be skewed. To illustrate the principles I focus on the demand for owner-occupied housing. Housing units are indivisible and heterogeneous while tastes for housing are at least partly idiosyncratic, causing housing markets to be thin. In addition, prior to 1998, capital gains tax provisions introduced a sharp kink in the budget constraint of existing owner-occupiers in search of a new home: previous homeowners under age 55 paid no capital gains tax if they bought up, but were subject to capital gains tax if they bought down. I first characterize the economic conditions under which households err on the up or down side when choosing a home in the presence of a thin market and a kinked budget constraint. I then specify an empirical model that takes such effects into account. Results based on Monte Carlo experiments indicate that failing to allow for skewness in the demand for housing leads to biased estimates of the elasticities of demand when such skewness is actually present. In addition, estimates based on American Housing Survey data suggest that such bias is substantial: controlling for skewness reduces the price elasticity of demand among previous owner-occupiers from 1.6 to 0.3. Moreover, 58% of previous homeowners err on the up while only 42% err on the down side. Thus, housing demand is skewed. / Ph. D.

The driving risk of novice teenagers is the highest during the initial period after licensure but decreases rapidly. This dissertation develops recurrent-event change-point models to detect the time when driving risk decreases significantly for novice teenager drivers. The dissertation consists of three major parts: the first part applies recurrent-event change-point models with identical change-points for all subjects; the second part proposes models to allow change-points to vary among drivers by a hierarchical Bayesian finite mixture model; the third part develops a non-parametric Bayesian model with a Dirichlet process prior. In the first part, two recurrent-event change-point models to detect the time of change in driving risks are developed. The models are based on a non-homogeneous Poisson process with piecewise constant intensity functions. It is shown that the change-points only occur at the event times and the maximum likelihood estimators are consistent. The proposed models are applied to the Naturalistic Teenage Driving Study, which continuously recorded textit{in situ} driving behaviour of 42 novice teenage drivers for the first 18 months after licensure using sophisticated in-vehicle instrumentation. The results indicate that crash and near-crash rate decreases significantly after 73 hours of independent driving after licensure. The models in part one assume identical change-points for all drivers. However, several studies showed that different patterns of risk change over time might exist among the teenagers, which implies that the change-points might not be identical among drivers. In the second part, change-points are allowed to vary among drivers by a hierarchical Bayesian finite mixture model, considering that clusters exist among the teenagers. The prior for mixture proportions is a Dirichlet distribution and a Markov chain Monte Carlo algorithm is developed to sample from the posterior distributions. DIC is used to determine the best number of clusters. Based on the simulation study, the model gives fine results under different scenarios. For the Naturalist Teenage Driving Study data, three clusters exist among the teenagers: the change-points are 52.30, 108.99 and 150.20 hours of driving after first licensure correspondingly for the three clusters; the intensity rates increase for the first cluster while decrease for other two clusters; the change-point of the first cluster is the earliest and the average intensity rate is the highest. In the second part, model selection is conducted to determine the number of clusters. An alternative is the Bayesian non-parametric approach. In the third part, a Dirichlet process Mixture Model is proposed, where the change-points are assigned a Dirichlet process prior. A Markov chain Monte Carlo algorithm is developed to sample from the posterior distributions. Automatic clustering is expected based on change-points without specifying the number of latent clusters. Based on the Dirichlet process mixture model, three clusters exist among the teenage drivers for the Naturalistic Teenage Driving Study. The change-points of the three clusters are 96.31, 163.83, and 279.19 hours. The results provide critical information for safety education, safety countermeasure development, and Graduated Driver Licensing policy making. / Ph. D.

The great social and economic impact of earthquakes has made necessary the development of novel structural health monitoring (SHM) solutions for increasing the level of structural safety and assessment. SHM is the process of comparing the current state of a structure’s condition relative to a healthy baseline state to detect the existence, location, and degree of likely damage during or after a damaging input, such as an earthquake. Many SHM algorithms have been proposed in the literature. However, a large majority of these algorithms cannot be implemented in real time. Therefore, their results would not be available during or immediately after a major event for urgent post-event response and decision making. Further, these off-line techniques are not capable of providing the input information required for structural control systems for damage mitigation. The small number of real-time SHM (RT-SHM) methods proposed in the past, resolve these issues. However, these approaches have significant computational complexity and typically do not manage nonlinear cases directly associated with relevant damage metrics. Finally, many available SHM methods require full structural response measurement, including velocities and displacements, which are typically difficult to measure. All these issues make implementation of many existing SHM algorithms very difficult if not impossible. This thesis proposes simpler, more suitable algorithms utilising a nonlinear Bouc-Wen hysteretic baseline model for RT-SHM of a large class of nonlinear hysteretic structures. The RT-SHM algorithms are devised so that they can accommodate different levels of the availability of design data or measured structural responses, and therefore, are applicable to both existing and new structures. The second focus of the thesis is on developing a high-speed, high-resolution, seismic structural displacement measurement sensor to enable these methods and many other SHM approaches by using line-scan cameras as a low-cost and powerful means of measuring structural displacements at high sampling rates and high resolution. Overall, the results presented are thus significant steps towards developing smart, damage-free structures and providing more reliable information for post-event decision making.

Cette thèse traite de quatre sujets en rapport avec les dynamiques non-réversibles. Chacun fait l'objet d'un chapitre qui peut être lu indépendamment.Le premier chapitre est une introduction générale présentant les problématiques et quelques résultats majeurs de physique statistique computationnelle.Le second chapitre concerne la résolution numérique d'équations aux dérivées partielles hypoelliptiques, c'est-à-dire faisant intervenir un opérateur différentiel inversible mais non coercif. Nous prouvons la consistance de la méthode de Galerkin ainsi que des taux de convergence pour l'erreur. L'analyse est également conduite dans le cas d'une formulation point-selle, qui s'avère être la plus adaptée dans les cas qui nous intéressent. Nous démontrons que nos hypothèses sont satisfaites dans un cas simple et vérifions numériquement nos prédictions théoriques sur cet exemple.Dans le troisième chapitre nous proposons une stratégie générale permettant de construire des variables de contrôle pour des dynamiques hors-équilibre. Cette méthode permet en particulier de réduire la variance des estimateurs de coefficient de transport par moyenne ergodique. Cette réduction de variance est quantifiée dans un régime perturbatif. La variable de contrôle repose sur la solution d'une équation aux dérivées partielles. Dans le cas de l'équation de Langevin cette équation est hypoelliptique, ce qui motive le chapitre précédent. La méthode proposée est testée numériquement sur trois exemples.Le quatrième chapitre est connecté au troisième puisqu'il utilise la même idée de variable de contrôle. Il s'agit d'estimer la mobilité d'une particule dans le régime sous-amorti, où la dynamique est proche d'être Hamiltonienne. Ce travail a été effectué en collaboration avec G. Pavliotis durant un séjour à l'Imperial College London.Le dernier chapitre traite des processus de Markov déterministes par morceaux, qui permettent l'échantillonnage de mesure en grande dimension. Nous prouvons la convergence exponentielle vers l'équilibre de plusieurs dynamiques de ce type sous un formalisme général incluant le processus de Zig-Zag (ZZP), l'échantillonneur à particule rebondissante (BPS) et la dynamique de Monte Carlo hybride randomisée (RHMC). La dépendances des bornes sur le taux de convergence que nous démontrons sont explicites par rapport aux paramètres du problème. Cela permet en particulier de contrôler la taille des intervalles de confiance pour des moyennes empiriques lorsque la dimension de l'espace des phases sous-jacent est grande. Ce travail a été fait en collaboration avec C. Andrieu, A. Durmus et N. Nüsken. / This thesis deals with four topics related to non-reversible dynamics. Each is the subject of a chapter which can be read independently. The first chapter is a general introduction presenting the problematics and some major results of computational statistical physics. The second chapter concerns the numerical resolution of hypoelliptic partial differential equations, i.e. involving an invertible but non-coercive differential operator. We prove the consistency of the Galerkin method as well as convergence rates for the error. The analysis is also carried out in the case of a saddle-point formulation, which is the most appropriate in the cases of interest to us. We demonstrate that our assumptions are met in a simple case and numerically check our theoretical predictions on this example. In the third chapter we propose a general strategy for constructing control variates for nonequilibrium dynamics. In particular, this method reduces the variance of transport coefficient estimators by ergodic mean. This variance reduction is quantified in a perturbative regime. The control variate is based on the solution of a partial differential equation. In the case of Langevin's equation this equation is hypoelliptic, which motivates the previous chapter. The proposed method is tested numerically on three examples. The fourth chapter is connected to the third since it uses the same idea of a control variate. The aim is to estimate the mobility of a particle in the underdamped regime, where the dynamics are close to being Hamiltonian. This work was done in collaboration with G. Pavliotis during a stay at Imperial College London. The last chapter deals with Piecewise Deterministic Markov Processes, which allow measure sampling in high-dimension. We prove the exponential convergence towards the equilibrium of several dynamics of this type under a general formalism including the Zig-Zag process (ZZP), the Bouncy Particle Sampler (BPS) and the Randomized Hybrid Monte Carlo (RHMC). The dependencies of the bounds on the convergence rate that we demonstrate are explicit with respect to the parameters of the problem. This allows in particular to control the size of the confidence intervals for empirical averages when the size of the underlying phase space is large. This work was done in collaboration with C. Andrieu, A. Durmus and N. Nüsken

Dans le cadre de cette thèse, notre préoccupation principale est de développer des approches non supervisées permettant de résoudre des problèmes de grande taille le plus efficacement possible. Pour ce faire, nous avons considéré des approches bayésiennes qui permettent d'estimer conjointement les paramètres de la méthode avec l'objet d'intérêt. Dans ce cadre, la difficulté principale est que la loi a posteriori est en général complexe. Pour résoudre ce problème, nous nous sommes intéressés à l'approximation bayésienne variationnelle (BV) qui offre une approximation séparable de la loi a posteriori. Néanmoins, les méthodes d’approximation BV classiques souffrent d’une vitesse de convergence faible. La première contribution de cette thèse consiste à transposer les méthodes d'optimisation par sous-espace dans l'espace fonctionnel impliqué dans le cadre BV, ce qui nous permet de proposer une nouvelle méthode d'approximation BV. Nous avons montré l’efficacité de notre nouvelle méthode par les comparaisons avec les approches de l’état de l’art.Nous avons voulu ensuite confronter notre nouvelle méthodologie à des problèmes de traitement d'images de grande taille. De plus nous avons voulu favoriser les images régulières par morceau. Nous avons donc considéré un a priori de Variation Total (TV) et un autre a priori à variables cachées ressemblant à un mélange scalaire de gaussiennes par changement de positions. Avec ces deux modèles a priori, en appliquant notre méthode d’approximation BV, nous avons développé deux approches non-supervisées rapides et bien adaptées aux images régulières par morceau.En effet, les deux lois a priori introduites précédemment sont corrélées ce qui rend l'estimation des paramètres de méthode très compliquée : nous sommes souvent confronté à une fonction de partition non explicite. Pour contourner ce problème, nous avons considéré ensuite de travailler dans le domaine des ondelettes. Comme les coefficients d'ondelettes des images naturelles sont généralement parcimonieux, nous avons considéré des lois de la famille de mélange scalaire de gaussiennes par changement d'échelle (GSM) pour décrire la parcimonie. Une autre contribution est donc de développer une approche non-supervisée pour les lois de la famille GSM dont la densité est explicitement connue, en utilisant la méthode d'approximation BV proposée. / In this thesis, our main objective is to develop efficient unsupervised approaches for large dimensional problems. To do this, we consider Bayesian approaches, which allow us to jointly estimate regularization parameters and the object of interest. In this context, the main difficulty is that the posterior distribution is generally complex. To tackle this problem, we consider variational Bayesian (VB) approximation, which provides a separable approximation of the posterior distribution. Nevertheless, classical VB methods suffer from slow convergence speed. The first contribution of this thesis is to transpose the subspace optimization methods to the functional space involved in VB framework, which allows us to propose a new VB approximation method. We have shown the efficiency of the proposed method by comparisons with the state of the art approaches. Then we consider the application of our new methodology to large dimensional problems in image processing. Moreover, we are interested in piecewise smooth images. As a result, we have considered a Total Variation (TV) prior and a Gaussian location mixture-like hidden variable model. With these two priors, using our VB approximation method, we have developed two fast unsupervised approaches well adapted to piecewise smooth images.In fact, the priors introduced above are correlated which makes the estimation of regularization parameters very complicated: we often have a non-explicit partition function. To sidestep this problem, we have considered working in the wavelet domain. As the wavelet coefficients of natural images are generally sparse, we considered prior distributions of the Gaussian scale mixture family to enforce sparsity. Another contribution is therefore the development of an unsupervised approach for a prior distribution of the GSM family whose density is explicitly known, using the proposed VB approximation method.

Lorsqu’on fait face à des systèmes non linéaires, les notions classiques de stabilité ne suffisent pas à garantir un comportement approprié vis-à-vis de problématiques telles que le suivi de trajectoires, la synchronisation et la conception d’observateurs. La stabilité incrémentale a été proposée en tant qu’outil permettant de traiter de tels problèmes et de garantir que le système présente des comportements qualitatifs pertinents. Cependant, comme c’est souvent le cas avec les systèmes non linéaires, la complexité de l’analyse conduit les ingénieurs à rechercher des relaxations, ce qui introduit du conservatisme. Dans cette thèse, nous nous intéressons à la stabilité incrémentale d’une classe spécifique de systèmes, à savoir les systèmes affines par morceaux, qui pourraient fournir un outil avantageux pour aborder la stabilité incrémentale de systèmes dynamiques plus génériques.Les systèmes affines par morceaux ont un espace d’états partitionné, et sa dynamique dans chaque région est régie par une équation différentielle affine. Ils peuvent représenter des systèmes contenant des non linéarités affines par morceaux, ainsi que servir comme des approximations de systèmes non linéaires plus génériques. Ce qui est plus important, leur description est relativement proche de celle des systèmes linéaires, ce qui permet d’obtenir des conditions d’analyse exprimées comme des inégalités matricielles linéaires qui peuvent être traités numériquement de façon efficace par des solveurs existants.Dans la première partie de ce document de thèse, nous passons en revue la littérature sur l’analyse des systèmes affines par morceaux en utilisant des techniques de Lyapunov et la dissipativité. Nous proposons ensuite de nouvelles conditions pour l’analyse du gain L2 incrémental et la stabilité asymptotique incrémentale des systèmes affines par morceaux exprimés en tant qu’inégalités matricielles linéaires. Ces conditions sont montrées être moins conservatives que les résultats précédents et sont illustrées par des exemples numériques.Dans la deuxième partie, nous considérons le cas des systèmes affines par morceaux incertains représentés comme l’interconnexion entre un système nominal et un bloc d’incertitude structuré. En utilisant la théorie de la séparation des graphes, nous proposons des conditions qui étendent le cadre des contraintes quadratiques intégrales afin de considérer le cas où le système nominal est affine par morceaux, à la fois dans les cas non incrémental et incrémental. Via la théorie de la dissipativité, ces conditions sont ensuite exprimées en tant qu’inégalités matricielles linéaires.Finalement, la troisième partie de ce document de thèse est consacrée à l’analyse de systèmes non linéaires de Lur’e incertains. Nous développons une nouvelle technique d’approximation permettant de réécrire ces systèmes de façon équivalente comme des systèmes affines par morceaux incertains connectés avec l’erreur d’approximation. L’approche proposée garantit que l’erreur d’approximation est Lipschitz continue avec la garantie d’une borne supérieure prédéterminée sur la constante de Lipschitz. Cela nous permet d’utiliser les techniques susmentionnées pour analyser des classes plus génériques de systèmes non linéaires. / When dealing with nonlinear systems, regular notions of stability are not enough to ensure an appropriate behavior when dealing with problems such as tracking, synchronization and observer design. Incremental stability has been proposed as a tool to deal with such problems and ensure that the system presents relevant qualitative behavior. However, as it is often the case with nonlinear systems, the complexity of the analysis leads engineers to search for relaxations, which introduce conservatism. In this thesis, we focus on the incremental stability of a specific class of systems, namely piecewise-affine systems, which could provide a valuable tool for approaching the incremental stability of more general dynamical systems.Piecewise-affine systems have a partitioned state space, in each region of which the dynamics are governed by an affine differential equation. They can represent systems containing piecewise-affine nonlinearities, as well as serve as approximations of more general nonlinear systems. More importantly, their description is relatively close to that of linear systems, allowing us to obtain analysis conditions expressed as linear matrix inequalities that can be efficiently handled numerically by existing solvers.In the first part of this memoir, we review the literature on the analysis of piecewise-affine systems using Lyapunov and dissipativity techniques. We then propose new conditions for the analysis of incremental L2-gain and incremental asymptotic stability of piecewise-affine systems expressed as linear matrix inequalities. These conditions are shown to be less conservative than previous results and illustrated through numerical examples.In the second part, we consider the case of uncertain piecewise-affine systems represented as the interconnection between a nominal system and a structured uncertainty block. Using graph separation theory, we propose conditions that extend the framework of integral quadratic constraints to consider the case when the nominal system is piecewise affine, both in the non-incremental and incremental cases. Through dissipativity theory, these conditions are then expressed as linear matrix inequalities.Finally, the third part of this memoir is devoted to the analysis of uncertain Lur’e-type nonlinear systems. We develop a new approximation technique allowing to equivalently rewrite such systems as uncertain piecewise-affine systems connected with the approximation error. The proposed approach ensures that the approximation error is Lipschitz continuous with a guaranteed pre-specified upper bound on the Lipschitz constant. This enables us to use the aforementioned techniques to analyze more general classes of nonlinear systems.

In this thesis we will investigate how the SBP-SAT finite difference method behave with and without an interface. As model problem, we consider the heat equation with piecewise constant coefficients. The thesis is split in two main parts. In the first part we look at the heat equation in one-dimension, and in the second part we expand the problem to a two-dimensional domain. We show how the SAT-parameters are chosen such that the scheme is dual consistent and stable. Then, we perform numerical experiments, now looking at the static case. In the one-dimensional case we see that the second order SBP-SAT method with an interface converge with an order of two, while the second order SBP-SAT method without an interface converge with an order of one.