This work presents a new approach that combines the comprehensive capabilities of the ensemble Kalman filter (EnKF) and the flow path information from streamlines to eliminate and/or reduce some of the problems and limitations of the use of the EnKF for history matching reservoir models. The recent use of the EnKF for data assimilation and assessment of uncertainties in future forecasts in reservoir engineering seems to be promising. EnKF provides ways of incorporating any type of production data or time lapse seismic information in an efficient way. However, the use of the EnKF in history matching comes with its shares of challenges and concerns. The overshooting of parameters leading to loss of geologic realism, possible increase in the material balance errors of the updated phase(s), and limitations associated with non-Gaussian permeability distribution are some of the most critical problems of the EnKF. The use of larger ensemble size may mitigate some of these problems but are prohibitively expensive in practice. We present a streamline-based conditioning technique that can be implemented with the EnKF to eliminate or reduce the magnitude of these problems, allowing for the use of a reduced ensemble size, thereby leading to significant savings in time during field scale implementation. Our approach involves no extra computational cost and is easy to implement. Additionally, the final history matched model tends to preserve most of the geological features of the initial geologic model. A quick look at the procedure is provided that enables the implementation of this approach into the current EnKF implementations. Our procedure uses the streamline path information to condition the covariance matrix in the Kalman Update. We demonstrate the power and utility of our approach with synthetic examples and a field case. Our result shows that using the conditioned technique presented in this thesis, the overshooting/undershooting problems disappears and the limitation to work with non- Gaussian distribution is reduced. Finally, an analysis of the scalability in a parallel implementation of our computer code is given.

Die dynamische Schätzung der Fahrzeugposition durch Sensordatenfusion ist eine der grundlegenden Aufgaben für moderne Verkehrsanwendungen wie zum Beispiel fahrerlose Transportsysteme oder Pre-Crash-Sicherheitssysteme. In dieser Arbeit wird ein Verfahren zur dezentralen kooperativen Fahrzeuglokalisierung vorgestellt, das auf einer allgemeinen Methode zur Fusion von Informationen mehrerer Teilnehmer beruht. Sowohl die lokale als auch die übertragene Schätzung wird durch Partikel dargestellt. Innerhalb einer Simulation wird gezeigt, dass sich die Positionsschätzung der einzelnen Teilnehmer im Netzwerk im Vergleich zu einer reinen GPS-basierten Lösung verbessert.

The objective of this study is to compare Bayesian and parametric approaches to determine the best for estimating reliability in complex systems. Determining reliability is particularly important in business and medical contexts. As expected, the Bayesian method showed the best results in assessing the reliability of systems. In the first study, the Bayesian reliability function under the Higgins-Tsokos loss function using Jeffreys as its prior performs similarly as when the Bayesian reliability function is based on the squared-error loss. In addition, the Higgins-Tsokos loss function was found to be as robust as the squared-error loss function and slightly more efficient. In the second study, we illustrated that--through the power law intensity function--Bayesian analysis is applicable in the power law process. The power law intensity function is the key entity of the power law process (also called the Weibull process or the non-homogeneous Poisson process). It gives the rate of change of a system's reliability as a function of time. First, using real data, we demonstrated that one of our two parameters behaves as a random variable. With the generated estimates, we obtained a probability density function that characterizes the behavior of this random variable. Using this information, under the commonly used squared-error loss function and with a proposed adjusted estimate for the second parameter, we obtained a Bayesian reliability estimate of the failure probability distribution that is characterized by the power law process. Then, using a Monte Carlo simulation, we showed the superiority of the Bayesian estimate compared with the maximum likelihood estimate and also the better performance of the proposed estimate compared with its maximum likelihood counterpart. In the next study, a Bayesian sensitivity analysis was performed via Monte Carlo simulation, using the same parameter as in the previous study and under the commonly used squared-error loss function, using mean square error comparison. The analysis was extended to the second parameter as a function of the first, based on the relationship between their maximum likelihood estimates. The simulation procedure demonstrated that the Bayesian estimates are superior to the maximum likelihood estimates and that the selection of the prior distribution was sensitive. Secondly, we found that the proposed adjusted estimate for the second parameter has better performance under a noninformative prior. In the fourth study, a Bayesian approach was applied to real data from breast cancer research. The purpose of the study was to investigate the applicability of a Bayesian analysis to survival time of breast cancer data and to justify the applicability of the Bayesian approach to this domain. The estimation of one parameter, the survival function, and hazard function were analyzed. The simulation analysis showed that the Bayesian estimate of the parameter performed better compared with the estimated value under the Wheeler procedure. The excellent performance of the Bayesian estimate is reflected even for small sample sizes. The Bayesian survival function was also found to be more efficient than its parametric counterpart. In the last study, a Bayesian analysis was carried out to investigate the sensitivity to the choice of the loss function. One of the parameters of the distribution that characterized the survival times for breast cancer data was estimated applying a Bayesian approach and under two different loss functions. Also, the estimates of the survival function were determined under the same setting. The simulation analysis showed that the choice of the squared-error loss function is robust in estimating the parameter and the survival function.

Spatial variation in tree community composition and assembly is due in large part to dispersal limitation, spatial variation in environmental conditions, and interactions among competing trees. The relative importance of these processes may be governed by landscape structure and environmental conditions. (I) The movement of frugivores between remnant forests and successional areas is vital for tropical forest tree species to colonize successional habitats. I found that avian frugivores crossing forest edges were generally insensitive to percent cover and clustering of pasture trees. If pastures were abandoned the distance from forest edges would not likely limit frugivore visitation and seed deposition under pasture trees in my study. (II) Relatively little is known from a theoretical conservation perspective about how reserve size affects communities assembled by abiotic and dispersal limitations. Simulated small reserve systems increased the distance between environments dominated by different species, diminishing the importance of source-sink dynamics. I found a trade-off between preserving different aspects of natural communities, with greater [alpha]-diversity in large reserves and greater [gamma]-diversity across small reserve systems. (III) Functional trait diversity of co-occurring organisms may be indicative of the processes that structure communities. Across spatial scales, an axis of leaf succulence exhibited the strongest evidence for niche-based assembly among co-occurring Ficus individuals, whereas specific leaf area (SLA) showed the strongest evidence for niche-based assembly among species. Trait analyses of co-occurring individuals had greater power than analyses at the species level, especially for traits with high intraspecific variation. Environmental filtering may be stronger at higher elevations due to drought stress. (IV) Individual fitness is a function of the interaction between traits and environment, or environmental selection. I estimated spatial selective gradients affecting a subtropical tree community and found that the trait axes with the strongest selection were also those with the least spatial variation. Interestingly, factors associated with selection were quite different for growth versus survivorship. The trait-by-environment interactions I identified are strong candidates for spatial niche differentiation, and may explain how tree species coexist in this diverse subtropical forest. / text

Mit der Liberalisierung des Strommarkts, den unsicheren Aussichten in der Klimapolitik und stark schwankenden Preisen bei Brennstoffen, Emissionsrechten und Kraftwerkskomponenten hat bei Kraftwerksinvestitionen das Risikomanagement an Bedeutung gewonnen. Dies äußert sich im vermehrten Einsatz probabilistischer Verfahren. Insbesondere bei regulativen Risiken liefert der klassische, häufigkeitsbasierte Wahrscheinlichkeitsbegriff aber keine Handhabe zur Risikoquantifizierung. In dieser Arbeit werden Kraftwerksinvestitionen und -portfolien in Deutschland mit Methoden des Bayes'schen Risikomanagements bewertet. Die Bayes'sche Denkschule begreift Wahrscheinlichkeit als persönliches Maß für Unsicherheit. Wahrscheinlichkeiten können auch ohne statistische Datenanalyse allein mit Expertenbefragungen gewonnen werden. Das Zusammenwirken unsicherer Werttreiber wurde mit einem probabilistischen DCF-Modell (Discounted Cash Flow-Modell) spezifiziert und in ein Einflussdiagramm mit etwa 1200 Objekten umgesetzt. Da der Überwälzungsgrad von Brennstoff- und CO2-Kosten und damit die Höhe der von den Kraftwerken erwirtschafteten Deckungsbeiträge im Wettbewerb bestimmt werden, reicht eine einzelwirtschaftliche Betrachtung der Kraftwerke nicht aus. Strompreise und Auslastungen werden mit Heuristiken anhand der individuellen Position der Kraftwerke in der Merit Order bestimmt, d.h. anhand der nach kurzfristigen Grenzkosten gestaffelten Einsatzreihenfolge. Dazu wurden 113 thermische Großkraftwerke aus Deutschland in einer Merit Order vereinigt. Das Modell liefert Wahrscheinlichkeitsverteilungen für zentrale Größen wie Kapitalwerte von Bestandsportfolien sowie Stromgestehungskosten und Kapitalwerte von Einzelinvestitionen (Steinkohle- und Braunkohlekraftwerke mit und ohne CO2-Abscheidung sowie GuD-Kraftwerke). Der Wert der Bestandsportfolien von RWE, E.ON, EnBW und Vattenfall wird primär durch die Beiträge der Braunkohle- und Atomkraftwerke bestimmt. Erstaunlicherweise schlägt sich der Emissionshandel nicht in Verlusten nieder. Dies liegt einerseits an den Zusatzgewinnen der Atomkraftwerke, andererseits an den bis 2012 gratis zugeteilten Emissionsrechten, welche hohe Windfall-Profite generieren. Dadurch erweist sich der Emissionshandel in seiner konkreten Ausgestaltung insgesamt als gewinnbringendes Geschäft. Über die Restlaufzeit der Bestandskraftwerke resultiert ab 2008 aus der Einführung des Emissionshandels ein Barwertvorteil von insgesamt 8,6 Mrd. €. In ähnlicher Dimension liegen die Barwertvorteile aus der 2009 von der Bundesregierung in Aussicht gestellten Laufzeitverlängerung für Atomkraftwerke. Bei einer achtjährigen Laufzeitverlängerung ergäben sich je nach CO2-Preisniveau Barwertvorteile von 8 bis 15 Mrd. €. Mit höheren CO2-Preisen und Laufzeitverlängerungen von bis zu 28 Jahren würden 25 Mrd. € oder mehr zusätzlich anfallen. Langfristig erscheint fraglich, ob unter dem gegenwärtigen Marktdesign noch Anreize für Investitionen in fossile Kraftwerke gegeben sind. Zu Beginn der NAP 2-Periode noch rentable Investitionen in Braunkohle- und GuD-Kraftwerke werden mit der auslaufenden Gratiszuteilung von Emissionsrechten zunehmend unrentabler. Die Rentabilität wird durch Strommarkteffekte der erneuerbaren Energien und ausscheidender alter Gas- und Ölkraftwerke stetig weiter untergraben. Steinkohlekraftwerke erweisen sich selbst mit anfänglicher Gratiszuteilung als riskante Investition. Die festgestellten Anreizprobleme für Neuinvestitionen sollten jedoch nicht dem Emissionshandel zugeschrieben werden, sondern resultieren aus den an Grenzkosten orientierten Strompreisen. Das Anreizproblem ist allerdings bei moderaten CO2-Preisen am größten. Es gilt auch für Kraftwerke mit CO2-Abscheidung: Obwohl die erwarteten Vermeidungskosten für CCS-Kraftwerke gegenüber konventionellen Kohlekraftwerken im Jahr 2025 auf 25 €/t CO2 (Braunkohle) bzw. 38,5 €/t CO2 (Steinkohle) geschätzt werden, wird ihr Bau erst ab CO2-Preisen von 50 bzw. 77 €/t CO2 rentabel. Ob und welche Kraftwerksinvestitionen sich langfristig rechnen, wird letztlich aber politisch entschieden und ist selbst unter stark idealisierten Bedingungen kaum vorhersagbar. / Power plant investors face large uncertainties due to ongoing liberalization, climate policy, and long investment horizons. This study provides a probabilistic appraisal of power plant investments within the framework of Bayesian decision theory. A Bayesian influence diagram is used for setting up a discounted cash flow model and analysing the profitability of power plants. As the study explicitly models merit order pricing, the pass-through of random fuel and carbon costs may be analysed. The study derives probabilistic statements about net present values of single investments and company portfolios and explores the sensitivity of profits to variations of select input variables. In the majority of cases, an increase in the price of emission allowances also increases the net present value of existing power plant portfolios. A substantially increased carbon prices also is the prerequisite to diversify power plant portfolios by gas and CCS plants. For the currently prevailing German electricity market, we argue that investors may lack incentives for new investments in fossil generation, a finding that holds true also with implementation of CCS. Our estimates are conservative, as profitability will further deteriorate with the build-up of renewables.

No description available.

In this thesis, we address issues of model estimation for longitudinal categorical data and of model selection for these data with missing covariates. Longitudinal survey data capture the responses of each subject repeatedly through time, allowing for the separation of variation in the measured variable of interest across time for one subject from the variation in that variable among all subjects. Questions concerning persistence, patterns of structure, interaction of events and stability of multivariate relationships can be answered through longitudinal data analysis. Longitudinal data require special statistical methods because they must take into account the correlation between observations recorded on one subject. A further complication in analysing longitudinal data is accounting for the non- response or drop-out process. Potentially, the missing values are correlated with variables under study and hence cannot be totally excluded. Firstly, we investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from the Longitudinal Survey of Immigrants to Australia. Data for each subject is observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia. Secondly, we examine the Bayesian model selection techniques of the Bayes factor and Deviance Information Criterion for our regression models with miss- ing covariates. Computing Bayes factors involve computing the often complex marginal likelihood p(y|model) and various authors have presented methods to estimate this quantity. Here, we take the approach of path sampling via power posteriors (Friel and Pettitt, 2006). The appeal of this method is that for hierarchical regression models with missing covariates, a common occurrence in longitudinal data analysis, it is straightforward to calculate and interpret since integration over all parameters, including the imputed missing covariates and the random effects, is carried out automatically with minimal added complexi- ties of modelling or computation. We apply this technique to compare models for the employment status of immigrants to Australia. Finally, we also develop a model choice criterion based on the Deviance In- formation Criterion (DIC), similar to Celeux et al. (2006), but which is suitable for use with generalized linear models (GLMs) when covariates are missing at random. We define three different DICs: the marginal, where the missing data are averaged out of the likelihood; the complete, where the joint likelihood for response and covariates is considered; and the naive, where the likelihood is found assuming the missing values are parameters. These three versions have different computational complexities. We investigate through simulation the performance of these three different DICs for GLMs consisting of normally, binomially and multinomially distributed data with missing covariates having a normal distribution. We find that the marginal DIC and the estimate of the effective number of parameters, pD, have desirable properties appropriately indicating the true model for the response under differing amounts of missingness of the covariates. We find that the complete DIC is inappropriate generally in this context as it is extremely sensitive to the degree of missingness of the covariate model. Our new methodology is illustrated by analysing the results of a community survey.

Doctor of Philosophy / Over the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival could be due to a number of real factors, including access to primary health care, the availability of diagnostic and treatment facilities and the treatment actually given, or to artefact, such as lead-time bias, stage migration, sampling error or measurement error. Likewise, a temporal increase in survival could be the result of earlier diagnosis and improved treatment of cancer; it could also be due to artefact after the introduction of screening programs (adding lead time), changes in the definition of cancer, stage migration or several of these factors, producing both real and artefactual trends. In this thesis, I report methods that I modified and applied, some technical issues in the use of such data, and an analysis of data from the State of New South Wales (NSW), Australia, illustrating their use in evaluating and potentially improving the quality of cancer care, showing how data quality might affect the conclusions of such analyses. This thesis describes studies of comparative survival based on population-based cancer registry data, with three published papers and one accepted manuscript (subject to minor revision). In the first paper, I describe a modified method for estimating spatial variation in cancer survival using empirical Bayes methods (which was published in Cancer Causes and Control 2004). I demonstrate in this paper that the empirical Bayes method is preferable to standard approaches and show how it can be used to identify cancer types where a focus on reducing area differentials in survival might lead to important gains in survival. In the second paper (published in the European Journal of Cancer 2005), I apply this method to a more complete analysis of spatial variation in survival from colorectal cancer in NSW and show that estimates of spatial variation in colorectal cancer can help to identify subgroups of patients for whom better application of treatment guidelines could improve outcome. I also show how estimates of the numbers of lives that could be extended might assist in setting priorities for treatment improvement. In the third paper, I examine time trends in survival from 28 cancers in NSW between 1980 and 1996 (published in the International Journal of Cancer 2006) and conclude that for many cancers, falls in excess deaths in NSW from 1980 to 1996 are unlikely to be attributable to earlier diagnosis or stage migration; thus, advances in cancer treatment have probably contributed to them. In the accepted manuscript, I described an extension of the work reported in the second paper, investigating the accuracy of staging information recorded in the registry database and assessing the impact of error in its measurement on estimates of spatial variation in survival from colorectal cancer. The results indicate that misclassified registry stage can have an important impact on estimates of spatial variation in stage-specific survival from colorectal cancer. Thus, if cancer registry data are to be used effectively in evaluating and improving cancer care, the quality of stage data might have to be improved. Taken together, the four papers show that creative, informed use of population-based cancer registry data, with appropriate statistical methods and acknowledgement of the limitations of the data, can be a valuable tool for evaluating and possibly improving cancer care. Use of these findings to stimulate evaluation of the quality of cancer care should enhance the value of the investment in cancer registries. They should also stimulate improvement in the quality of cancer registry data, particularly that on stage at diagnosis. The methods developed in this thesis may also be used to improve estimation of geographical variation in other count-based health measures when the available data are sparse.

NY, New York Univ., Dep. of Economics, Diss.--New York, 2003. / Kopie, ersch. im Verl. UMI, Ann Arbor, Mich.

Techn. University, Diss., 2004--München.