The Bootstrap of Mean for Dependent Heterogeneous Arrays.

Presently, conditions ensuring the validity of bootstrap methods for the sample mean of (possibly heterogeneous) near epoch dependent (NED) functions of mixing processes are unknown. Here we establish the validity of the bootstrap in this context, extending the applicability of bootstrap methods to a class of processes broadly relevant for applications in economics and finance. Our results apply to two block bootstrap methods: the moving blocks bootstrap of Künsch ( 989) and Liu and Singh ( 992), and the stationary bootstrap of Politis and Romano ( 994). In particular, the consistency of the bootstrap variance estimator for the sample mean is shown to be robust against heteroskedasticity and dependence of unknown form. The first order asymptotic validity of the bootstrap approximation to the actual distribution of the sample mean is also established in this heterogeneous NED context.

Reported Job Satisfaction : What Does It Mean?

By reporting his satisfaction with his job or any other experience, an individual does not communicate the number of utils that he feels. Instead, he expresses his posterior preference over available alternatives conditional on acquired knowledge of the past. This new interpretation of reported job satisfaction restores the power of microeconomic theory without denying the essential role of discrepancies between one’s situation and available opportunities. Posterior human wealth discrepancies are found to be the best predictor of reported job satisfaction. Static models of relative utility and other subjective well-being assumptions are all unambiguously rejected by the data, as well as an \"economic\" model in which job satisfaction is a measure of posterior human wealth. The \"posterior choice\" model readily explains why so many people usually report themselves as happy or satisfied, why both younger and older age groups are insensitive to current earning discrepancies, and why the past weighs more heavily than the present and the future.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Efficient estimation using the characteristic function : theory and applications with high frequency data

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Vector boson scattering at high energy at the LHC

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Vector flow mapping using plane wave ultrasound imaging

Les diagnostics cliniques des maladies cardio-vasculaires sont principalement effectués à l’aide d’échographies Doppler-couleur malgré ses restrictions : mesures de vélocité dépendantes de l’angle ainsi qu’une fréquence d’images plus faible à cause de focalisation traditionnelle. Deux études, utilisant des approches différentes, adressent ces restrictions en utilisant l’imagerie à onde-plane, post-traitée avec des méthodes de délai et sommation et d’autocorrélation. L’objectif de la présente étude est de ré-implémenté ces méthodes pour analyser certains paramètres qui affecte la précision des estimations de la vélocité du flux sanguin en utilisant le Doppler vectoriel 2D. À l’aide d’expériences in vitro sur des flux paraboliques stationnaires effectuées avec un système Verasonics, l’impact de quatre paramètres sur la précision de la cartographie a été évalué : le nombre d’inclinaisons par orientation, la longueur d’ensemble pour les images à orientation unique, le nombre de cycles par pulsation, ainsi que l’angle de l’orientation pour différents flux. Les valeurs optimales sont de 7 inclinaisons par orientation, une orientation de ±15° avec 6 cycles par pulsation. La précision de la reconstruction est comparable à l’échographie Doppler conventionnelle, tout en ayant une fréquence d’image 10 à 20 fois supérieure, permettant une meilleure caractérisation des transitions rapides qui requiert une résolution temporelle élevée.

Potential use of a recombinant replication-defective adenovirus vector carrying the C-terminal portion of the P97 adhesin protein as a vaccine against Mycoplasma hyopneumoniae in swine.

Mycoplasma hyopneumoniae causes severe economic losses to the swine industry worldwide and the prevention of its related disease, enzootic porcine pneumonia, remains a challenge. The P97 adhesin protein of M. hyopneumoniae should be a good candidate for the development of a subunit vaccine because antibodies produced against P97 could prevent the adhesion of the pathogen to the respiratory epithelial cells in vitro. In the present study, a P97 recombinant replication-defective adenovirus (rAdP97c) subunit vaccine efficiency was evaluated in pigs. The rAdP97c vaccine was found to induce both strong P97 specific humoral and cellular immune responses. The rAdP97c vaccinated pigs developed a lower amount of macroscopic lung lesions (18.5 ± 9.6%) compared to the unvaccinated and challenged animals (45.8 ± 11.5%). rAdP97c vaccine reduced significantly the severity of inflammatory response and the amount of M. hyopneumoniae in the respiratory tract. Furthermore, the average daily weight gain was slightly improved in the rAdP97c vaccinated pigs (0.672 ± 0.068 kg/day) compared to the unvaccinated and challenged animals (0.568 ± 0.104 kg/day). A bacterin-based commercial vaccine (Suvaxyn® MH-one) was more efficient to induce a protective immune response than rAdP97c even if it did not evoke a P97 specific immune response. These results suggest that immunodominant antigens other than P97 adhesin are also important in the induction of a protective immune response and should be taken into account in the future development of M. hyopneumoniae subunit vaccines.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.