Medical cost has received increasing interest recently in Biostatistics and public health. Statistical analysis and inference of life time medical cost have been challenging by the fact that the survival times are censored on some study subjects and their subsequent cost are unknown. Huang (2002) proposed the calibration regression model which is a semiparametric regression tool to study the medical cost associated with covariates. In this thesis, an inference procedure is investigated using empirical likelihood ratio method. The unadjusted and adjusted empirical likelihood confidence regions are constructed for the regression parameters. We compare the proposed empirical likelihood methods with normal approximation based method. Simulation results show that the proposed empirical likelihood ratio method outperforms the normal approximation based method in terms of coverage probability. In particular, the adjusted empirical likelihood is the best one which overcomes the under coverage problem.

Germination rates and germination fractions of seeds can be predicted well by the hydrothermal time (HTT) model. Its four parameters hydrothermal time, minimum soil temperature, minimum soil moisture, and variation of minimum soil moisture, however, must be determined by lengthy germination experiments at combinations of several levels of soil temperature and moisture. For some applications of the HTT model it is more important to have approximate estimates for many species rather than exact values for only a few species. We suggest that minimum temperature and variation of minimum moisture can be estimated from literature data and expert knowledge. This allows to derive hydrothermal time and minimum moisture from existing data from germination experiments with one level of temperature and moisture. We applied our approach to a germination experiment comparing germination fractions of wild annual species along an aridity gradient in Israel. Using this simplified approach we estimated hydrothermal time and minimum moisture of 36 species. Comparison with exact data for three species shows that our method is a simple but effective method for obtaining parameters for the HTT model. Hydrothermal time and minimum moisture supposedly indicate climate related germination strategies. We tested whether these two parameters varied with the climate at the site where the seeds had been collected. We found no consistent variation with climate across species, suggesting that variation is more strongly controlled by site-specific factors. / Keimungsgeschwindigkeit und Anteil gekeimter Samen lassen sich gut mit dem Hydrothermalzeit-Modell bestimmen. Dessen vier Parameter Hydrothermalzeit, Mindesttemperatur, Mindestbodenfeuchte und Streuung der Mindestbodenfeuchte müssen jedoch durch aufwendige Keimungsversuche bei Kombinationen von mehreren Temperatur- und Feuchtigkeitsstufen bestimmt werden. Für manche Anwendungen des Hydrothermalzeit-Modells sind aber ungefähre Werte für viele Arten wichtiger als genaue Werte für wenige Arten. Wenn die Mindesttemperatur und die Streuung der Mindestfeuchte aus Veröffentlichungen und Expertenwissen geschätzt würde, können die Hydrothermalzeit und Mindestbodenfeuchte aus vorhandenen Daten von Keimungsversuchen mit nur einer Temperatur- und Feuchtigkeitsstufe berechnet werden. Wir haben unseren Ansatz auf einen Keimungsversuch zum Vergleich der Keimungsquote wilder einjähriger Arten entlang eines Trockenheitsgradienten in Israel angewendet. Mit diesem Ansatz bestimmten wir die Hydrothermalzeit und Mindestfeuchtigkeit von 36 Arten. Der Vergleich mit genauen Werten für drei Arten zeigt, dass mit unserem Ansatz Hydrothermalzeit-Parameter einfach und effektiv bestimmt werden können. Hydrothermalzeit und Mindestfeuchtigkeit sollten auch bestimmte klimabedingte Keimungsstrategien anzeigen. Deshalb testeten wir, ob diese zwei Parameter mit dem Klima am Ursprungsort der Samen zusammenhängen. Wir fanden jedoch keinen für alle Arten übereinstimmenden Zusammenhang, so dass die Unterschiede vermutlich stärker durch standörtliche als durch klimatische Ursachen hervorgerufen werden.

In order to cope with the turbulent environments in digital age, an enterprise should response to the changes quickly. Therefore, an enterprise must improve her ability of real-time decision-making. One way to increase the competence of real-time decision-making is to use Real-Time Decision Support Systems (RTDSS). A key feature for a Decision Support Systems (DSS) to successfully support real-time decision-making is to help decision-makers selecting the best models within deadline. This study focuses on developing methods to support the mechanism of model selection in DSS. There are five results in this study. Firstly, we have developed a time-based framework to evaluate models. This framework can help decision-makers to evaluate the quality and cost of model solutions. Secondly, based on the framework of models evaluation, we also developed three models selection strategies. These strategies can help decision-makers to select the best model within deadline. Thirdly, according the definitions of parameter value precision and model solution precision in this study, we conduct a simulation analysis to understand the impacts of the precision of parameter values to the precision of a model solution. Fourthly, in order to understand the interaction among the model selection variables, we also simulate the application of model selection strategies. The results of simulation indicate our study can support models selection well. Finally, we developed a structure-based model retrieval method to help decision-makers find alternative models from model base efficiently and effectively. In conclusion, the results of this research have drawn a basic skeleton for the development of models selection. This research also reveals much insight into the development of real-time decision support systems.

The contributions of the research are (a) an infrastructure of data acquisition systems that provides the necessary information for an automated EMS system enabling autonomous distributed state estimation, model validation, simplified protection, and seamless integration of other EMS applications, (b) an object-oriented, interoperable, and unified component model that can be seamlessly integrated with a variety of applications of the EMS, (c) a distributed dynamic state estimator (DDSE) based on the proposed data acquisition system and the object-oriented, interoperable, and unified component model, (d) a physically-based synchronous machine model, which is expressed in terms of the actual self and mutual inductances of the synchronous machine windings as a function of rotor position, for the purpose of synchronous machine parameters identification, and (e) a robust and highly efficient algorithm for the optimal power flow (OPF) problem, one of the most important applications of the EMS, based on the validated states and models of the power system provided by the proposed DDSE.

This paper empirically investigates the propagation of business sentiment within the European Union (EU) and adds to the literature on shock absorption via a common market's real economy. To this end, we combine EU-wide official business sentiment indicators with world input-output (IO) data and information on indirect wage costs. Econometrically, we model interdependencies in economic activities via IO-linkages and apply space-time models. The resulting evidence provides indication for the existence of substantial spillovers in business sentiment formation. Accordingly, and highlighted by the estimated impacts of changes in indirect labor costs, policy reforms aiming at increasing the resilience of the European single market need to take these spillovers into account in order to increase its effectiveness. / Series: Department of Economics Working Paper Series

This study focused on stress and resilience among teachers in 15 urban secondary schools serving areas of multiple and complex disadvantage in the Greater Manchester and Merseyside regions of England (UK). It utilised the mixed-methods approach to gather and analyse the data. This consisted of a questionnaire survey of 150 teachers and interviews of 20 teachers. It examined person characteristics of teachers in these schools; key stress risks in the schools; coping strategies employed by these teachers and their protective factors. The main quantitative analysis methodologies used in the study were descriptive analysis; factor analysis and regression analysis. Qualitative findings were analysed using thematic analysis and teacher pen portrait and school case study presentations. Integrative analysis of quantitative and qualitative findings was then conducted in the discussion of main findings. This study found that the teachers’ major force characteristics were self-efficacy; persistence; personal attitudes towards one’s job; personality and temperament and commitment to the job. Their main resource characteristic was experience while their major demand characteristic was their professional role. These teachers were also exposed to person, proximal processes, context and time risks. There were risks associated with force and demand person characteristics. The main process risks were within their interactions with pupils, parents, colleagues and senior management. There were also context risks in their microsystem, mesosystem, exosystem and macrosystem. There were also time risks across the microtime, mesotime and macrotime. To cope, teachers in this sample utilised both direct-action and palliative coping strategies. Results also indicated that these teachers’ protective factors were in their resource and force characteristics; proximal processes; context and time.

Contingent Convertibles (CoCos) are a new type of hybrid debt instrument characterized by forced equity conversion or write-down under a specified trigger event, usually indicating a state of near non-viability of the Additional Tier 1 capital category, giving them additional features such as possible coupon cancellation. In this thesis, the structure of CoCos is presented and different pricing approaches are introduced. A special focus is put on structural models with the Analytically Tractable First Passage Time(AT1P) Model and its extensions. Two models are applied on the write-down CoCo issued by Svenska Handelsbanken, starting with the equity derivative model and followed by the AT1P model. / Contingent Convertibles (Cocos) - villkorade konvertibla obligationer, är en ny typ av hybridinstrument som kännetecknas av konvertering till eget kapital eller nedskrivning av lånet vid en viss utlösande händelse, som vanligtvis indikerar ett tillstånd där den emitterande banken har behov av att absorbera förluster. Under strikta villkor kan dessa CoCo obligationer tillhöra primärkapital, där de kännetecknas av bland annat möjlig inställning av kuponger. I denna avhandling presenteras CoCons struktur och olika prissättningsmodeller läggs fram. Ett särskilt fokus läggs på strukturella modeller med “Analytically Tractable First Passage Time (AT1P) Model” och dess utvidgningar. Två modeller tillämpas på CoCon emitterad av Svenska Handelsbanken: “equity derivative” modellen och AT1P modellen.

Simulation tools have progressed largely and in modern times they are commonly usedby engineers to design and simulate machines or part of machines before building and deploying them in the field. The field of Hardware-in-the-loop (HIL) is gaining significant interest among companies as they strive to enhance product safety and reliability simul-taneously reducing testing costs and accelerated development speed. This study presents the Real Time simulation improvements effectuated to the Steer and Brake by wire system on an underground face drill rig. These improvements in the model are validated with a comparison between simulated environment and real test data from the machine using a cosimulation between Matlab&Simulink with AMESim. At the end, this improved model is prepared to be compatible with an Hardware-in-the-loop application that requires an adequate computational time.

Survival analysis is an effective tool for conservation studies, since it measure the risk of an event that is important for the survival of populations and preservation of biodiversity. In this thesis three different models for survival analysis are used to estimate the age at breeding between two genetically different populations of brown trout. These populations are an evolutionary enigma, since they apparently coexist in direct competition with each other, which according to ecological theory should not happen. Thus it is of interest if differences between them can be identified. The data consists of brown trouts and has been collected over 20 years. The models are the Cox Proportional Hazards model, the Complementary Log-Log Link model and the Log Logistic Accelerated Failure-Time model. The Cox model were estimated in three different ways due to the nonproportional hazards in the estimates of time to breeding, which gave different interpretations of the same model. All of the models agree that the population B breed at younger ages than the population A, which suggests that the two populations have different reproductive strategies.

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