We consider a class of queueing systems that consist of server pools in parallel and multiple customer classes. Customer service times are assumed to be exponentially distributed. We study the asymptotic behavior of these queueing systems in a heavy traffic regime that is known as the Halfin and Whitt many-server asymptotic regime. Our main contribution is a general framework for establishing state space collapse results in the Halfin and Whitt many-server asymptotic regime for parallel server systems having multiple customer classes. In our work, state space collapse refers to a decrease in the dimension of the processes tracking the number of customers in each class waiting for service and the number of customers in each class being served by various server pools. We define and introduce a state space collapse function, which governs the exact details of the state space collapse. Our methodology is similar in spirit to that in Bramson (1998); however, Bramson studies an asymptotic regime in which the number of servers is fixed and Bramson does not require a state space collapse function. We illustrate the applications of our results in three different parallel server systems. The first system is a distributed parallel server system under the minimum-expected-delay faster-server-first (MED-FSF) or minimumexpected- delay load-balancing (MED-LB) policies. We prove that the MED-FSF policy minimizes the stationary distribution of total number of customers in the system. However, under the MED-FSF policy all the servers in our distributed system except those with the lowest service rate experience 100% utilization but under the MED-LB policy, on the other hand, the utilizations of all the server pools are equal. The second system we consider is known as the N-model. We show that when the service times only depend on the server pool providing service a static priority rule is asymptotically optimal. Finally, we study two results conjectured in the literature for V-systems. We show for all of these systems that the conditions on the hydrodynamic limits can easily be checked using the standard tools that have been developed in the literature to analyze fluid models.

The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily maximum temperatures of Umeå, and compare the performance of some methods in forecasting these temperatures. Here we analyse the data of daily maximum temperatures and find the predictions for some local period using methods of autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and cubic splines.  The forecast package in R is used for this purpose and automatic forecasting methods available in the package are applied for modelling with ARIMA, ETS, and cubic splines. The thesis begins with some initial modelling on univariate time series of daily maximum temperatures. The data of daily maximum temperatures of Umeå from 2008 to 2013 are used to compare the methods using various lengths of training period. On the basis of accuracy measures we try to choose the best method. Keeping in mind the fact that there are various factors which can cause the variability in daily temperature, we try to improve the forecasts in the next part of thesis by using multivariate time series forecasting method on the time series of maximum temperatures together with some other variables. Vector auto regressive (VAR) model from the vars package in R is used to analyse the multivariate time series. Results: ARIMA is selected as the best method in comparison with ETS and cubic smoothing splines to forecast one-step-ahead daily maximum temperature of Umeå, with the training period of one year. It is observed that ARIMA also provides better forecasts of daily temperatures for the next two or three days. On the basis of this study, VAR (for multivariate time series) does not help to improve the forecasts significantly. The proposed ARIMA with one year training period is compatible with the forecasts of daily maximum temperature of Umeå obtained from Swedish Meteorological and Hydrological Institute (SMHI).

Bewertungen von Immobilien sollen den Marktwert einschätzen und sind notwendig für Kauf-, Verkaufs- und Bauentscheidungen, für die Kreditvergabe und für die Besteuerung. Trotz dieser eindeutigen Aufgabenstellung existierten unterschiedliche Verfahren, mit welchen Marktwerte ermittelt werden können. Ein Bewertungsverfahren soll einerseits mit ökonomischer Theorie vereinbar sein und andererseits Bewertungen generieren, die beobachtete Transaktionspreise gut vorhersagen. Die Dissertation analysiert die drei wichtigsten Bewertungsansätze Sachwert-, Vergleichswert- und Ertragswertverfahren, zeigt das jeweils zugrundeliegende Marktmodell und evaluiert die kodifizierten Verfahren nach der Wertermittlungsverordnung (WertV) anhand von beobachteten Transaktionen. Darüber hinaus gibt die Dissertation einen Überblick zu Immobilienpreisindizes und zu hedonischen Methoden. Für die ökonometrischen Analysen wurden umfangreiche Daten zum Berliner Immobilienmarkt verwendet. / Appraisals should assess the market value of properties and are necessary for buying, selling or building decisions, for lending and for taxation. Despite this unambiguous task different techniques exist for ascertaining market values. An valuation approach should be in accordance with economic theory and should generate appraisals, which are reliable estimates for transaction prices. This dissertation analyzes the three most important valuation approaches, i.e. cost, sales comparison, and income approach, shows the underlying market models and evaluates the valuation techniques that are codified in the German Regulation on Valuation (WertV). For the latter evaluations, appraisals are compared with observed transaction prices. In addition, the dissertation gives an overview on real estate price indices and on the hedonic approach. Extensive data on Berlin's real estate market are used for the econometric analysis.

This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR model estimated on data aggregated to quarterly frequency and a quarterly least squares VAR with standard parametrization. Forecasts are evaluated using root mean squared errors and the log-determinant of the forecast error covariance matrix. The results indicate that the inclusion of monthly data improves the accuracy of quarterly forecasts of monthly variables for horizons up to a year. For quarterly variables the one and two quarter forecasts are improved when using monthly data.

Business to Business (B2B) firms spend significant resources managing close relationships with their customers, yet there is limited understanding of how the customers perceive the relationship based on the customer management efforts initiated by the firm. Specifically, studies on how firms communicate different values to B2B customers and how they perceive the values the firm offers by consistently evaluating the direct marketing communication which ultimately affect their buying behaviors have been largely overlooked. Typically, the direct marketing communication efforts are geared towards explicitly featuring economic values or relational values. To implement an effective communication strategy catering to customers’ preferences, firms should understand how these organizational marketing communications dynamically influence the perceived importance of different values offered by the firm. Therefore, using data from a Fortune 500 B2B service firm and employing a content analysis and a robust econometric model, we find that (i) the effect of economic and relational marketing communication on customer purchase behavior vary by customers and change overtime (ii) the latent stock variable of direct marketing communication affect the customer purchase behaviors and (iii) the evolution of customers’ perceived importance can be recovered using the transaction data. Overall, we provide a marketing resource reallocation strategy that enables marketers to customize marketing communication and improve a firm’s financial performance.

The study was aimed to provide information on local biomass development during crop growth using ground based optical sensors and to incorporate the local crop status to a crop growth simulation model to improve understanding on inherent variability of crop field. The experiment was conducted in a farmer’s field located near Princeton in Caldwell County, Western Kentucky. Data collection on soil, crop and weather variables was carried out in the farm from 2006 December to 2008 October. During this period corn (Zea mays L.) and winter wheat (Triticum sp) were grown in the field. A 450 m long representative transect across the field consisting of 45 locations each separated by 10 m was selected for the study. Soil water content was measured in a biweekly interval during crop growth from these locations. Measurements on crop growth parameters such as plant height, tiller count, biomass and grain yield were able to show spatial variability in crop biomass and grain yield production. Crop reflectance measured at important crop growth stages. Soil water sensing capacitance probe was site specifically calibrated for each soil depth in each location. Various vegetation indices were calculated as proxy variables of crop growth. Inherent soil properties such as soil texture and elevation were found playing a major role in influencing spatial variability in crop yield mainly by affecting soil water storage. Temporal persistence of spatial patterns in soil water storage was not observed. Optimum spatial correlation structure was observed between crop growth parameters and optical sensor measurements collected early in the season and aggregated at 2*2 m2 sampling area. NDVI, soil texture, soil water storage and different crop growth parameters were helpful in explaining the spatial processes that influence grain yield and biomass using state space analysis. DSSAT was fairly sensitive to reflect site specific inputs on soil variability in crop production.

Contemporary theories frame emotion as an intra-personal system comprised of subcomponents such as experience, expressive behaviors, and physiology that interact over time to give rise to emotional episodes. Emotional episodes occur in the context of a social interaction or an ongoing relationship making it important to also conceptualize the inter-personal emotion system in which the subcomponents of the emotional response interact not only within the individual but across the partners as well. Emotion theory has been constricted by a dominant linear information processing metaphor and has not yet fully embraced a dynamic systems approach integrating concepts of open, self-organizing systems to interpersonal emotion regulation processes. To address these limitations, this study examined the emergence of structure and patterns in real-time dyadic interactions between pairs of female strangers where one partner is purposefully regulating her emotional responding. One member of each dyad was randomly assigned to suppress, positively reappraise, or act normally during an interaction task. Three subcomponents of emotion were examined (expressive behaviors, experience, and physiology) along with three features of dynamic systems (attractor basins, flexibility/entropy, and physiological linkage). Results indicate differences in the emergence of structure and patterns in real-time dyadic interactions that varies by emotional responding type. Suppression dyads were characterized by a non-emotional response attractor, reduced behavioral flexibility, stronger physiological linkage as compared to control and reappraisal dyads. Reappraisal dyads expressed more positive emotions during the interaction than control or suppression dyads, and reappraisal partners showed evidence of positive physiological linkage with the reappraiser. In conclusion, structural patterns do differ by emotion regulation condition indicating the importance of intrapersonal phenomena on the emergence of interpersonal system dynamics.

Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process.

<p>The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.</p><p>Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.</p> / Dissertation

This thesis considers the role of prestige in international relations through a case study of the early years of the American space programme. After discussing the dearth of literature on the place of prestige in international relations, it examines the effect of prestige considerations on the space policy of the Eisenhower and Kennedy Administrations. Although urged to pursue a spectacular space programme for the sake of American prestige, Eisenhower believed that international prestige was not a matter requiring government intervention. Consequently, he favoured a small civilian space programmme structured according to scientific rather than political criteria. The opposite was the case for President Kennedy. It is shown how his experience in Congress, followed by his 1960 election campaign, gave him a different perspective on the political importance and utility of international prestige. This was appreciated by advocates of a manned lunar landing, who were able to present the Apollo project to him in terms of its international prestige value. Finally, after noting the changing attitude towards the space programme between 1961 and 1963, as well as the diminished American interest in international prestige by 1969, the thesis compares the experiences of the two administrations to draw some conclusions about the factors necessary for prestige to play an important role in international policy.