Econometric applications of empirical likelihood

We provide some evidence of Empirical Likelihood's (EL) practical value in econometrics. We present EL as an alternative to GMM estimation and assess the finite-sample properties of their overidentification tests (size and power) through Monte Carlo simulations. We address the issue of the importance of the results to applied workers and use as laboratories to our experiments two settings with potential empirical applications: the Mean-Variance and Three-Moment CAPM and a dynamic panel model with individual effects. In cases in which we found important size distortions we introduced efficient bootstrap critical values. Prior research applied this bootstrapping technique to the GMM (GMM-bootstrap) and we present results for the EL (EL-bootstrap). We also include an empirical example on a United States panel cash-flow model. Even if our findings do not uniformly support the conclusion that one estimator dominates the other, we found evidence that EL and EL-bootstrap are good alternatives to GMM and GMM-bootstrap in some econometric application

Echantillonnage compressé le long de trajectoires physiquement plausibles en IRM

Magnetic Resonance Imaging~(MRI) is a non-invasive and non-ionizing imaging technique that provides images of body tissues, using the contrast sensitivity coming from the magnetic parameters (T$_1$, T$_2$ and proton density). Data are acquired in the $k$-space, corresponding to spatial Fourier frequencies. Because of physical constraints, the displacement in the $k$-space is subject to kinematic constraints. Indeed, magnetic field gradients and their temporal derivative are upper bounded. Hence, the scanning time increases with the image resolution. Decreasing scanning time is crucial to improve patient comfort, decrease exam costs, limit the image distortions~(eg, created by the patient movement), or decrease temporal resolution in functionnal MRI. Reducing scanning time can be addressed by Compressed Sensing~(CS) theory. The latter is a technique that guarantees the perfect recovery of an image from undersampled data in $k$-space, by assuming that the image is sparse in a wavelet basis. Unfortunately, CS theory cannot be directly cast to the MRI setting. The reasons are: i) acquisition~(Fourier) and representation~(wavelets) bases are coherent and ii) sampling schemes obtained using CS theorems are composed of isolated measurements and cannot be realistically implemented by magnetic field gradients: the sampling is usually performed along continuous or more regular curves. However, heuristic application of CS in MRI has provided promising results. In this thesis, we aim to develop theoretical tools to apply CS to MRI and other modalities. On the one hand, we propose a variable density sampling theory to answer the first inpediment. The more the sample contains information, the more it is likely to be drawn. On the other hand, we propose sampling schemes and design sampling trajectories that fulfill acquisition constraints, while traversing the $k$-space with the sampling density advocated by the theory. The second point is complex and is thus addressed step by step. First, we propose continuous sampling schemes based on random walks and on travelling salesman~(TSP) problem. Then, we propose a projection algorithm onto the space of constraints that returns the closest feasible curve of an input curve~(eg, a TSP solution). Finally, we provide an algorithm to project a measure onto a set of measures carried by parameterizations. In particular, if this set is the one carried by admissible curves, the algorithm returns a curve which sampling density is close to the measure to project. This designs an admissible variable density sampler. The reconstruction results obtained in simulations using this strategy outperform existing acquisition trajectories~(spiral, radial) by about 3~dB. They permit to envision a future implementation on a real 7~T scanner soon, notably in the context of high resolution anatomical imaging.L'imagerie par résonance magnétique (IRM) est une technique d'imagerie non invasive et non ionisante qui permet d'imager et de discriminer les tissus mous grâce à une bonne sensibilité de contraste issue de la variation de paramètres physiques (T$_1$, T$_2$, densité de protons) spécifique à chaque tissu. Les données sont acquises dans l'espace-$k$, correspondant aux fréquences spatiales de l'image. Des contraintes physiques et matérielles contraignent le mode de fonctionnement des gradients de champ magnétique utilisés pour acquérir les données. Ainsi, ces dernières sont obtenues séquentiellement le long de trajectoires assez régulières (dérivée et dérivée seconde bornées). En conséquence, la durée d'acquisition augmente avec la résolution recherchée de l'image. Accélérer l'acquisition des données est crucial pour réduire la durée d'examen et ainsi améliorer le confort du sujet, diminuer les coûts, limiter les distorsions dans l'image~(e.g., dues au mouvement), ou encore augmenter la résolution temporelle en IRM fonctionnelle. L'échantillonnage compressif permet de sous-échantillonner l'espace-$k$, et de reconstruire une image de bonne qualité en utilisant une hypothèse de parcimonie de l'image dans une base d'ondelettes. Les théories d'échantillonnage compressif s'adaptent mal à l'IRM, même si certaines heuristiques ont permis d'obtenir des résultats prometteurs. Les problèmes rencontrés en IRM pour l'application de cette théorie sont i) d'une part, les bases d'acquisition~(Fourier) et de représentation~(ondelettes) sont cohérentes ; et ii) les schémas actuellement couverts par la théorie sont composés de mesures isolées, incompatibles avec l'échantillonnage continu le long de segments ou de courbes. Cette thèse vise à développer une théorie de l'échantillonnage compressif applicable à l'IRM et à d'autres modalités. D'une part, nous proposons une théorie d'échantillonnage à densité variable pour répondre au premier point. Les échantillons les plus informatifs ont une probabilité plus élevée d'être mesurés. D'autre part, nous proposons des schémas et concevons des trajectoires qui vérifient les contraintes d'acquisition tout en parcourant l'espace-$k$ avec la densité prescrite dans la théorie de l'échantillonnage à densité variable. Ce second point étant complexe, il est abordé par une séquence de contributions indépendantes. D'abord, nous proposons des schémas d'échantillonnage à densité variables le long de courbes continues~(marche aléatoire, voyageur de commerce). Ensuite, nous proposons un algorithme de projection sur l'espace des contraintes qui renvoie la courbe physiquement plausible la plus proche d'une courbe donnée~(e.g., une solution du voyageur de commerce). Nous donnons enfin un algorithme de projection sur des espaces de mesures qui permet de trouver la projection d'une distribution quelconque sur l'espace des mesures porté par les courbes admissibles. Ainsi, la courbe obtenue est physiquement admissible et réalise un échantillonnage à densité variable. Les résultats de reconstruction obtenus en simulation à partir de cette méthode dépassent ceux associées aux trajectoires d'acquisition utilisées classiquement~(spirale, radiale) de plusieurs décibels (de l'ordre de 3~dB) et permettent d'envisager une implémentation prochaine à 7~Tesla notamment dans le contexte de l'imagerie anatomique haute résolution

System identification and optimal control for mixed-mode cooling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004."September 2004."Includes bibliographical references (p. 285-294).The majority of commercial buildings today are designed to be mechanically cooled. To make the task of air conditioning buildings simpler, and in some cases more energy efficient, windows are sealed shut, eliminating occupants' direct access to fresh air. Implementation of an alternative cooling strategy-mixed-mode cooling-is demonstrated in this thesis to yield substantial savings in cooling energy consumption in many U.S. locations. A mixed-mode cooling strategy is one that relies on several different means of delivering cooling to the occupied space. These different means, or modes, of cooling could include: different forms of natural ventilation through operable windows, ventilation assisted by low-power fans, and mechanical air conditioning. Three significant contributions are presented in this thesis. A flexible system identification framework was developed that is well-suited to accommodate the unique features of mixed-mode buildings. Further, the effectiveness of this framework was demonstrated on an actual multi- zone, mixed-mode building, with model prediction accuracy shown to exceed that published for other naturally ventilated or mixed-mode buildings, none of which exhibited the complexity of this building. Finally, an efficient algorithm was constructed to optimize control strategies over extended planning horizons using a model-based approach. The algorithm minimizes energy consumption subject to the constraint that indoor temperatures satisfy comfort requirements. The system identification framework was applied to another mixed-mode building, where it was found that the aspects integral to the modeling framework led to prediction improvements relative to a simple model.(cont.) Lack of data regarding building apertures precluded the use of the model for control purposes. An additional contribution was the development of a procedure for extracting building time constants from experimental data in such a way that they are constrained to be physically meaningful.by Henry C. Spindler.Ph.D