The healthcare labor market has experienced some significant changes in the last half century, including the establishment of Medicare and Medicaid in 1965, the emergence of managed care in the 1980s, and the worldwide mobility of labor encouraged by globalization. Currently, more than 25% of physicians working in the U.S. are foreign-born. The existing body of literature related to the impact of immigration on local wages has to date found conflicting results. The purpose of this research is to evaluate the impact of immigration of foreign physicians on local physician wages. This study employs physician survey data from the AMA Physician Masterfile for the years 1997 through 2007 combined with wage data published by the Bureau of Labor Statistics and data from other government sources. Several econometric models are employed to analyze the wage impacts of immigration, including ordinary least squares, fixed effects, two-stage least squares and a first-difference approach to control for endogenous location choice. The results of this study provide evidence that in the short-run, the impacts of immigration of physicians on area wages is small but positive. In the long run, however, wages adjust and the impact becomes negative and statistically significant, although the magnitude of the impact of a one percentage point increase in the share of immigrant physicians in an area is less than 0.2%. The negative wage effects of immigration tend to be larger for foreign-born physicians educated in the U.S. compared with foreign-born international medical graduates. The study also finds evidence that the negative effects of immigration tend to be offset by outflows of the lowest paid native physicians. Furthermore, physicians tend to locate in areas where wages are already higher, and foreign-born physicians are more likely than their native counterparts to work in larger cities as opposed to rural areas. The research has important policy implications in the presence of current debate over immigration law and healthcare reform and in an era of increasing mobility of labor due to globalization.

Essay I: This essay provides an overview of the history of the Swedish pension system. Starting with the implementation of the public pension system in 1913, it outlines the key components of each major pension reform up until today along with a discussion of the main trade-offs and concerns that policy makers have faced. It also describes the historical background of the four largest occupational pension plans in Sweden and the mutual influence between these plans and the public pension system.        Essay II: Despite the fact that the increasing involvement of the private sector in pension provision has brought more flexibility to the pay-out phase of retirement, little is known about the characteristics of those who choose to annuitize their pension wealth and those who do not. I combine unique micro-data from a large Swedish occupational pension plan with rich national administrative data to study the choice between life annuities and fixed-term payouts with a minimum payout length of 5 years for 183,000 retiring white-collar workers. I find that low accumulation of assets is strongly associated with the choice of the 5-year payout. Consistent with individuals selecting payout length based on private information about their mortality prospects, individuals who choose the 5-year payout are in worse health, exhibit higher ex-post mortality rates and have shorter-lived parents than annuitants. Individuals also seem to respond to large, tax-induced changes in annuity prices.            Essay III: This essay estimates the causal effect of postponing retirement on a wide range of health outcomes using Swedish administrative data on cause-specific mortality, hospitalizations and drug prescriptions. Exogenous variation in retirement timing comes from a reform which raised the age at which broad categories of Swedish local government workers were entitled to retire with full pension benefits from 63 to 65. The reform caused a remarkable shift in the retirement distribution of the affected workers, increasing the actual retirement age by more than 4.5 months. Instrumental variable estimation results show no effect of postponing retirement on the overall consumption of health care, nor on the risk of dying early. There is evidence, however, of a reduction in diabetes-related hospitalizations and in the consumption of drugs that treat anxiety. Essay IV (with Per Engström): The consumption based method to estimate underreporting among self-employed, introduced by Pissarides and Weber (1989), is one of the workhorses in the empirical literature on tax evasion/avoidance. We show that failure to account for transitory income fluctuations in current income may overestimate the degree of underreporting by around 40 percent. Previous studies typically use instrumental variable methods to address the issue. In contrast, our access to registry based longitudinal income measures allows a direct approach based on more permanent income measures. This also allows us to evaluate the performance of a list of instruments widely used in the previous literature. Our analysis shows that capital income is the most suitable instrument in our application, while education and housing related measures do not seem to satisfy the exclusion restrictions.

System identification is used in engineering sciences to build mathematical models from data. A common issue in system identification problems is that the true inputs to the system are not fully known. In this thesis, existing approaches to unknown input problems are classified and some of their properties are analyzed.  A new indirect framework is proposed to treat system identification problems with unknown inputs. The effects of the unknown inputs are assumed to be measured through possibly unknown dynamics. Furthermore, the measurements may also be dependent on other known or measured inputs and can in these cases be called indirect input measurements. Typically, these indirect input measurements can arise when a subsystem of a larger system is of interest and only a limited set of sensors is available. Two examples are when it is desired to estimate parts of a mechanical system or parts of a dynamic network without full knowledge of the signals in the system. The input measurements can be used to eliminate the unknown inputs from a mathematical model of the system through algebraic manipulations. The resulting indirect model structure only depends on known and measured signals and can be used to estimate the desired dynamics or properties. The effects of using the input measurements are analyzed in terms of identifiability, consistency and variance properties. It is shown that cancelation of shared dynamics can occur and that the resulting estimation problem is similar to errors-in-variables and closed-loop estimation problems because of the noisy inputs used in the model. In fact, the indirect framework unifies a number of already existing system identification problems that are contained as special cases. For completeness, an instrumental variable method is proposed as one possibility for estimating the indirect model. It is shown that multiple datasets can be used to overcome certain identifiability issues and two approaches, the multi-stage and the joint identification approach, are suggested to utilize multiple datasets for estimation of models. Furthermore, the benefits of using the indirect model in filtering and for control synthesis are briefly discussed.  To show the applicability, the framework is applied to the roll dynamics of a ship for tracking of the loading conditions. The roll dynamics is very sensitive to changes in these conditions and a worst-case scenario is that the ship will capsize.  It is assumed that only motion measurements from an inertial measurement unit (IMU) together with measurements of the rudder angle are available. The true inputs are thus not available, but the measurements from the IMU can be used to form an indirect model from a well-established ship model. It is shown that only a subset of the unknown parameters can be estimated simultaneously. Data was collected in experiments with a scale ship model in a basin and the joint identification approach was selected for this application due to the properties of the model. The approach was applied to the collected data and gave promising results. / Till skillnad från många andra industrier där avancerade styrsystem har haft en omfattande utveckling under de senaste decennierna så har styrsystem för skepps- och marinindustrin inte alls utvecklats i samma utsträckning. Det är framförallt under de senaste 10 åren som lagkrav och stigande driftskostnader har ökat intresset för effektivitet och säkerhet genom användning av styrsystem. Rederier och den marina industrin är nu intresserade av hur de avancerade styrsystem som används inom andra områden kan tillämpas för marina ändamål. Huvudmålet är typiskt att minska den totala energianvändningen, och därmed också bränsleförbrukningen, genom att hela tiden planera om hur skeppet skall användas med hjälp av ny information samt styra skeppet och dess ingående system på ett sätt som maximerar effektiviteten. För många av dessa avancerade styrsystem är det grundläggande att ha en god förståelse för beteendet hos det systemet som skall styras. Ofta används matematiska modeller av systemet för detta ändamål. Sådana modeller kan skapas genom att observera hur systemet reagerar på yttre påverkan och använda dessa observationer för att finna eller skatta den modell som bäst beskriver observationerna. Observationerna är mätningar som görs med så kallade sensorer och tekniken att skapa modeller från mätningarna kallas för systemidentifiering. Detta är i grunden ett utmanande problem och det kan försvåras ytterligare om de sensorer som behövs inte finns tillgängliga eller är för dyra att installera. I denna avhandling föreslås en ny teknik där de mätningar som finns tillgängliga används på ett nytt och annorlunda sätt. Detta kan minska mängden nödvändiga sensorer eller möjliggöra användandet av alternativa sensorer i modell-framtagningen. Med hjälp av denna nya teknik kan enkla sensorer användas för att skatta en matematisk modell för en del av skeppet på ett sätt som inte är möjligt med traditionella metoder. Genom att skatta denna modell kan fysikaliska egenskaper hos skeppet, så som dess massa och hur massan är fördelad över skeppet, övervakas för att upptäcka förändringar. Just dessa två egenskaper har stor inverkan på hur skeppet beter sig och om skeppet är fellastat kan det i värsta fall kapsejsa. Vetskapen om dessa fysikaliska egenskaper kan alltså utöver effektivisering användas för att varna besättningen eller påverka styrsystemen så att farliga manövrar undviks. För att visa att tekniken fungerar i verkligheten har den använts på mätningar som har samlats in från ett skalenligt modellskepp. Experimenten utfördes i bassäng och resultaten visar att tekniken fungerar. Denna nya teknik är inte specifik för marint bruk utan kan också vara användbar i andra typer av tillämpningar. Även i dessa tillämpningar möjliggörs användandet av färre eller alternativa sensorer för att skatta modeller. Tekniken kan vara speciellt användbar när en modell av ett system eller process som verkar i ett nätverk av många system är av intresse, något som också diskuteras i avhandlingen.

La médecine fondée sur les preuves a conféré à l’essai contrôlé randomisé (ECR) le plus haut niveau de preuve dans l’évaluation de l’effet des médicaments, et par extension de toute intervention en santé. Cependant, le recours aux études observationnelles s’avère également nécessaire (i) pour conforter, en situation réelle, les résultats issus des ECR dont la validité externe est limitée, (ii) dans des situations, notamment lorsqu’il s’agit d’interventions complexes, où l’ECR n’est pas toujours réalisable pour des questions éthiques et/ou organisationnelles. Toutefois, les études observationnelles sont sujettes à différents types de biais, et notamment au biais d’indication. Ce travail de thèse explore les différentes techniques d’analyse statistique des résultats permettant de maîtriser ce biais. Dans une première partie, les aspects théoriques ont été abordés. Les différentes techniques disponibles ont été identifiées, analysées et comparées : les techniques d’ajustement multivarié, celles utilisant un score de propension (SP) et celles utilisant une variable instrumentale (VI). Pour approfondir les connaissances sur la question, une revue systématique de la littérature a été effectuée. Elle a mis en évidence la faible concordance entre les résultats obtenus en utilisant un SP et ceux obtenus en utilisant une VI, lorsque ces deux techniques étaient utilisées dans une même étude pour évaluer la même intervention. Dans une seconde partie, l’utilisation de SP et/ou VI a été testée dans trois exemples d’évaluation d’interventions complexes à partir de données de pratiques courantes recueillies dans le cadre de deux études observationnelles de cohorte : (i) l’évaluation de l’effet d’un réseau de soins spécialisé dans l’insuffisance cardiaque (IC) sur la mortalité ; (ii) l’évaluation de l’effet des stratégies médicamenteuses appropriées dans l’IC sur la mortalité ; (iii) l’évaluation de l’effet des stratégies antithrombotiques chez les patients hémodialysés sur le risque hémorragique. / Evidence-based medicine placed randomized controlled trials (RCT) at the highest level of evidence to evaluate the effects of medications and, by extension, of all health interventions. Nevertheless, observational studies are necessary (i) to support, in real-world settings, the results of RCTs, the external validity of which is limited, and (ii) in situations where RCTs are not feasible for ethical or practical reasons, particularly when evaluating complex interventions. However, observational studies are particularly prone to confounding by indication. This thesis focuses on analytical methods to reduce this bias. In its first part, the theoretical aspects were addressed. Available methods were identified, reviewed and compared: multivariate adjustment methods, methods using a propensity score (PS) and methods using an instrumental variable (IV). To further knowledge on this issue, a systematic literature review was performed. This review revealed that more and more observational studies simultaneously use PS and IV approaches to evaluate the same intervention, often leading to nonconcordant results that may be dif?cult to interpret. In a second part, the use of PS and/or VI methods was tested in three evaluations of complex interventions in real-world settings, using data from two cohort studies: (i) to evaluate the effectiveness on mortality of a community-based multidisciplinary disease management programme for heart failure (HF) patients; (ii) to evaluate the effectiveness of recommended drug prescriptions on mortality in patients with HF; (iii) to evaluate the effect of antiplatelet and anticoagulant therapies on the risk of major bleeding events in chronic hemodialysis patients

Les travaux présentés dans ce mémoire traitent de l'identification des systèmes dynamiques représentés sous la forme de modèles linéaires continus à paramètres variant lentement au cours du temps. La complexité du problème d'identification provient d'une part du caractère inconnu de la loi de variation des paramètres et d'autre part de la présence de bruits de nature inconnue sur les signaux mesurés. Les solutions proposées s'appuient sur une combinaison judicieuse du filtre de Kalman en supposant que les variations des paramètres peuvent être représentées sous la forme d'une marche aléatoire et de la méthode de la variable instrumentale qui présente l'avantage d'être robuste vis à vis de la nature des bruits de mesure. Les algorithmes de type récursif sont développés dans un contexte d'identification en boucle ouverte et en boucle fermée. Les différentes variantes se distinguent par la manière dont est construit la variable instrumentale. Inspirée de la solution développée pour les systèmes linéaires à temps invariant, une construction adaptative de la variable instrumentale est suggérée pour pouvoir suivre au mieux l'évolution des paramètres. Les performances des méthodes développées sont évaluées à l'aide de simulations de Monte Carlo et montrent la suprématie des solutions proposées s'appuyant sur la variable instrumentale par rapport celles plus classiques des moindres carrés récursifs. Les aspects pratiques et d'implantation numérique sont d'une importance capitale pour obtenir de bonnes performances lorsque ces estimateurs sont embarqués. Ces aspects sont étudiés en détails et plusieurs solutions sont proposées non seulement pour robustifier les estimateurs vis à vis du choix des hyper-paramètres mais également vis à vis de leur implantation numérique. Les algorithmes développés sont venus enrichir les fonctions de la boîte à outils CONTSID pour Matlab. Enfin, les estimateurs développés sont exploités pour faire le suivi de paramètres de deux systèmes physiques : un benchmark disponible dans la littérature constitué d'un filtre électronique passe-bande et une vanne papillon équipant les moteurs de voiture. Les deux applications montrent le potentiel des approches proposées pour faire le suivi de paramètres physiques variant lentement dans le temps / The work presented in this thesis deals with the identification of dynamic systems represented through continuous-time linear models with slowly time-varying parameters. The complexity of the identification problem comes on the one hand from the unknown character of the parameter variations and on the other hand from the presence of noises of unknown nature on the measured signals. The proposed solutions rely on a judicious combination of the Kalman filter assuming that the variations of the parameters can be represented in the form of a random walk, and the method of the instrumental variable which has the advantage of being robust with respect to the nature of the measurement noises. The recursive algorithms are developed in an open-loop and closed-loop identification setting. The different variants are distinguished by the way in which the instrumental variable is built. Inspired by the solution developed for time-invariant linear systems, an adaptive construction of the instrumental variable is suggested in order to be able to follow the evolution of the parameters as well as possible. The performance of the developed methods are evaluated using Monte Carlo simulations and show the supremacy of the proposed solutions based on the instrumental variable compared with the more classical least squares based approaches. The practical aspects and implementation issues are of paramount importance to obtain a good performance when these estimators are used. These aspects are studied in detail and several solutions are proposed not only to robustify the estimators with respect to the choice of hyperparameters but also with respect to their numerical implementation. The algorithms developed have enhanced the functions of the CONTSID toolbox for Matlab. Finally, the developed estimators are considered in order to track parameters of two physical systems: a benchmark available in the literature consisting of a bandpass electronic filter and a throttle valve equipping the car engines. Both applications show the potential of the proposed approaches to track physical parameters that vary slowly over time

L’industrie moderne fait largement appel à des robots industriels afin de réduire les coûts, ou encore améliorer la productivité et la qualité par exemple. Pour ce faire, une haute précision et une grande vitesse sont simultanément nécessaires. La conception de lois de commande conformes à de telles exigences demande une modélisation mathématique précise de ces robots. A cette fin, des modèles dynamiques sont construits à partir de données expérimentales. L’objectif de cette thèse est ainsi de fournir aux ingénieurs roboticiens des outils automatiques pour l’identification de bras robotiques. Dans cette perspective, une analyse comparative des méthodes existantes pour l’identification de robot est réalisée. Les avantages et inconvénients de chaque méthode sont ainsi mis en exergue. À partir de ces observations, les contributions sont articulées selon trois axes. Premièrement, l’étude porte sur l’estimation des vitesses et accélérations des corps du robot à partir de la position mesurée. Ces informations sont en effet nécessaires à la construction du modèle. La méthode usuelle est basée sur prétraitement "sur mesure" qui requière une connaissance fiable des bande-passantes du système, alors que celui-ci est encore inconnu. Pour surmonter ce dilemme, nous proposons une méthode capable d’estimer les dérivées automatiquement sans réglage préalable par l’utilisateur. Le deuxième axe concerne l’identification du contrôleur. Sa connaissance est en effet requise par la grande majorité des méthodes d’identification. Malheureusement, pour des raisons de propriété industrielle, il n’est pas toujours accessible. Pour traiter ce problème, deux méthodes sont introduites. Leur principe de base est d’identifier la loi de commande dans un premier temps avant d’identifier le modèle dynamique du bras robotique dans un second temps. La première méthode consiste à identifier la loi de commande de manière paramétrique, alors que la seconde fait appel à une identification non-paramétrique. Finalement, le troisième axe porte sur le réglage "sur mesure" du filtre decimate. L’identification du filtre de bruit est introduite en s’inspirant des méthodes développées par la communauté d’identification de systèmes. Ceci permet l’estimation automatique des paramètres dynamiques avec de faibles covariances tout en apportant une connaissance concernant la circulation du bruit à travers le système en boucle-fermée. Toutes les méthodes proposées sont validées sur un robot industriel à six degrés de liberté. Des perspectives sont esquissées pour de futurs travaux portant sur l’identification de systèmes robotiques, voire d’autres applications. / In modern manufacturing, industrial robots are essential components that allow saving cost, increase quality and productivity for instance. To achieve such goals, high accuracy and speed are simultaneously required. The design of control laws compliant with such requirements demands high-fidelity mathematical models of those robots. For this purpose, dynamic models are built from experimental data. The main objective of this thesis is thus to provide robotic engineers with automatic tools for identifying dynamic models of industrial robot arms. To achieve this aim, a comparative analysis of the existing methods dealing with robot identification is made. That allows discerning the advantages and the limitations of each method. From those observations, contributions are presented on three axes. First, the study focuses on the estimation of the joint velocities and accelerations from the measured position, which is required for the model construction. The usual method is based on a home-made prefiltering process that needs a reliable knowledge of the system’s bandwidths, whereas the system is still unknown. To overcome this dilemma, we propose a method able to estimate the joint derivatives automatically, without any setting from the user. The second axis is dedicated to the identification of the controller. For the vast majority of the method its knowledge is indeed required. Unfortunately, for copyright reasons, that is not always available to the user. To deal with this issue, two methods are suggested. Their basic philosophy is to identify the control law in a first step before identifying the dynamic model of the robot in a second one. The first method consists in identifying the control law in a parametric way, whereas the second one relies on a non-parametric identification. Finally, the third axis deals with the home-made setting of the decimate filter. The identification of the noise filter is introduced similarly to methods developed in the system identification community. This allows estimating automatically the dynamic parameters with low covariance and it brings some information about the noise circulation through the closed-loop system. All the proposed methodologies are validated on an industrial robot with 6 degrees of freedom. Perspectives are outlined for future developments on robotic systems identification and other complex problems.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-17T12:44:32Z No. of bitstreams: 1 Hugo Alves Velozo.pdf: 5196080 bytes, checksum: 14e9edcc07c0256cf726b1d0f7eb9a02 (MD5) / Made available in DSpace on 2017-04-17T12:44:32Z (GMT). No. of bitstreams: 1 Hugo Alves Velozo.pdf: 5196080 bytes, checksum: 14e9edcc07c0256cf726b1d0f7eb9a02 (MD5) Previous issue date: 2017-02-20 / CAPES / Most methods of identifcation of closed-loop dynamic systems are developed for linear and deterministic systems. However, most closed loop systems are nonlinear dynamic systems. In addition, such systems are subject to stochastic perturbations. Considering this problem, this work presents a methodology for the identifcation of closed loop stochastic nonlinear systems. For this purpose, the proposed methodology uses a local approach to identify nonlinear dynamic systems, that is, a set of Box-Jenkins local models are used to identify the dynamics of the nonlinear system. In this work, the nonlinear system is modeled through a Takagi-Sugeno fuzzy inference system, where the parameters of the antecedent of the fuzzy rules are estimated with the fuzzy clustering algorithm GustafsonKessel and the consequent Box-Jenkins model parameters are estimated with the fuzzy fuzzy RIV (Refned Instrumental Variable) and fuzzy IVARMA (Instrumental Variable ARMA) algorithms. The proposed method is applied in the identifcation of a closed-loop nonlinear thermal plant. / A maioria dos métodos de identifcação de sistemas dinâmicos em malha fechada são desenvolvidos para sistemas lineares e determinísticos. Entretanto, a maioria dos sistemas operando em malha fechada são sistemas dinâmicos não lineares. Além disso, esses sistemas estão sujeitos a perturbações de natureza estocástica. Considerando essa problemática, este trabalho apresenta uma metodologia para identifcação de sistemas não lineares estocásticos em malha fechada. Para isso, a metodologia proposta utiliza uma abordagem local de identifcação de sistemas dinâmicos não lineares, ou seja, um conjunto de modelos locais Box-Jenkins são utilizados para identifcar a dinâmica do sistema não linear. Neste trabalho, o sistema não linear é modelado por meio de um sistema de inferência fuzzy Takagi-Sugeno, onde os parâmetros do antecedente das regras fuzzy são estimados com o algoritmo de agrupamento fuzzy Gustafson-Kessel e o parâmetros do modelo Box-Jenkins do consequente são estimados com os algoritmos RIV (Refned Instrumental Variable) fuzzy e IVARMA (Instrumental Variable ARMA) fuzzy. O método proposto é aplicado na identifcação de uma planta térmica não linear em malha fechada.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-09-04T18:35:15Z No. of bitstreams: 1 OrlandoRochaFilho.pdf: 10104010 bytes, checksum: 7c750a6e03597fc2e7f7474b62c35a46 (MD5) / Made available in DSpace on 2017-09-04T18:35:15Z (GMT). No. of bitstreams: 1 OrlandoRochaFilho.pdf: 10104010 bytes, checksum: 7c750a6e03597fc2e7f7474b62c35a46 (MD5) Previous issue date: 2017-04-24 / This thesis presents a maximum likelihood based modeling approach applied to dynamic systems operating in non-stationary environment that uses recursive parametric estimation based on the method of fuzzy instrumental variable. The context is evolving and the idea is to guarantee a robust for estimation of the parameters of noise-corrupted experimental data. The methodology consists of an evolving fuzzy clustering algorithm based on the similarity of the data which employs an adaptive distance norm based on the maximum likelihood criterion that use an adaptive search strategy on the experiment in order to avoid the curse of dimensionality related to the number of rules created during data clustering of the data set. The computational and experimental results to exemplify the proposed methodology are: statistical analysis of the fuzzy instrumental variable inserted in the evolving context; black box modeling of a thermal plant; identification of a benchmark nonlinear system widely published in the literature and the black box modeling of a 2DOF helicopter. These examples are used to illustrate the performance and efficiency by operating in a non–stationary environment. / Nesta tese é apresentada uma proposta de modelagem baseada máxima verossimilhança aplicada a sistemas dinâmicos operando em ambiente não-estacionário que utiliza a estima- ção paramétrica recursiva baseada no método de variável instrumental nebulosa, inserido no contexto evolutivo, no sentido de garantir robustez para estimação dos parâmetros diante de dados experimentais corrompidos por ruído. A metodologia é composta por um algoritmo de agrupamento nebuloso evolutivo baseado na similaridade dos dados que emprega uma norma de distância adaptativa baseada no critério de máxima verossimilhança que utiliza uma estratégia de busca adaptativa no experimento para evitar o problema da maldição de dimensionalidade relacionada ao número de regras criadas durante o agrupamento do conjunto de dados. Os resultados computacionais e experimentais para exemplificação da metodologia proposta são: análise estatística da variável instrumental nebulosa inserida no contexto evolutivo; na modelagem caixa preta de uma planta térmica (processo térmico); identificação de um sistema não-linear amplamente divulgado na literatura e a modelagem caixa preta de um helicóptero com dois graus de liberdade que ilustra o desempenho e a eficiência operando ambiente não-estacionário.

No description available.

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies. / February 2009