Thesis (M.S.)--Boise State University, 2009. / Includes abstract. Includes bibliographical references (leaf 34).

Thesis (Ph. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains x, 86 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 84-86).

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on August 3, 2009). Includes bibliographical references (p. 65-68).

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Quality of life (QoL) is now firmly recognized as a significant outcome measure in public health, clinical and patient care research (1, 2). Despite a growing trend in conducting longitudinal QoL studies, the longitudinal changes in QoL in the general population remain poorly understood due to the limited number of studies. Furthermore, few studies have discussed the use of different statistical methods in analyzing the longitudinal change in QoL. This paper aimed to discuss the application of traditional statistical approach: R-ANOVA and newer statistical approaches: LMM and LGCA in analyzing the longitudinal change in QoL. The underlying assumptions, characteristics and specifications of each of the statistical methods were explained. Different public health studies that examined the longitudinal change of QoL would be elaborated in order to show how the criterions of each statistical method were fulfilled in the research analysis. Additionally, the limitations of applying the traditional statistical approach: R-ANOVA and the newer statistical approaches: LMM and LGCA in analyzing longitudinal QoL data will be discussed with the emphasis on how each analytical method overcome the weaknesses of one another. The understanding of the application of different statistical approaches in analyzing the longitudinal change in QoL can advance the future development of a robust statistical approach for QoL research. / published_or_final_version / Community Medicine / Master / Master of Public Health

Regarding the relationships among different insurance claims, especially in non-life insurance, the dependence behaviour in various models has been studied extensively. In this thesis, some discrete-time risk models with dependence structures would be investigated. One traditional discrete-time risk model is the time series risk model, in which the dependence would be on two aspects: time correlated claims and dependent business classes. A general vector (multivariate) autoregressive moving average (VARMA) model would be adopted to analyze the ruin probability of a surplus process. An upper bound for the ruin probability is derived for the general order of multivariate time series models in claims. Simulation studies are carried out for model comparison for finite time ruin probabilities. Another class of risk model is the compound binomial risk model, where the dependence structure would be based on the existence of a so-called by-claim in the claim process. The by-claim could be incurred in the same period as the main insurance claim, or it would be incurred in the next period, depending on a certain probability. A randomized dividend payment scheme with some fixed threshold value in surplus level would also be considered in this thesis. A methodology is discovered to obtain the Gerber-Shiu expected penalty function for the extended model. The final model investigated in this thesis is the periodic time series risk model. The periodic structure of the model gives a practical interpretation of the business cycle, in which there are high season and low season for the business. Some lower order periodic time series models are considered for the claim structures. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

The identification of hazardous road locations is important to the improvement of road safety. However, there is still no consensus on the best method of identifying hazardous road locations. While traditional methods, such as the hot spot methodology, focus on the physical distances separating road crashes only, the hot zone methodology takes network contiguity into consideration and treats contiguous road segments as hazardous road locations. Compared with the hot spot method, hot zone methodology is a relatively new direction and there still remain a number of methodological issues in applying the method to the identification of hazardous road locations. Hence, this study aims to provide a GIS-based study on the identification of crash hot zones as hazardous road locations with both link-attribute and event-based approaches. It first explores the general procedures of the two approaches in identifying traffic crash hot zones, and then investigates the characteristics of the two approaches by conducting a range of sensitivity analysis on defining threshold value and crash intensity with both simulated and empirical data. The results suggest that it is better to use a dissolved road network instead of a raw-link-node road network. The segmentation length and the interval of reference points have great impacts on the identification of hot zones, and they are better defined as 100 meters considering the stabilities of the performance. While employing a numerical definition to identify hot zones is a simple and effort-saving approach, using the Monte Carlo method can avoid selection bias in choosing an appropriate number as the threshold value. If the two approaches are compared, it is observed that the link-attribute approach is more likely to cause false negative problem and the event-based approach is prone to false positive problem around road junctions. No matter which method is used, the link-attribute approach requires less computer time in identifying crash hot zones. When a range of environmental variables have to be taken into consideration, the link-attribute approach is superior to the event-based approach in that it is easier for the link-attribute approach to incorporate environmental variables with statistical models. By investigating the hot zone methodology, this research is expected to enrich the theoretical knowledge of the identification of hazardous road locations and to practically provide policy-makers with more information on identifying road hazards. Further research efforts have to be dedicated to the ranking of hot zones and the investigation of false positive and false negative problems. / published_or_final_version / Geography / Doctoral / Doctor of Philosophy

Continual technology innovations make it possible to fabricate electronic devices on the order of 10nm. In this nanoscale regime, quantum physics becomes critically important, like energy quantization effects of the narrow channel and the leakage currents due to tunneling. It has also been utilized to build novel devices, such as the band-to-band tunneling field-effect transistors (FETs). Therefore, it presages accurate quantum transport simulations, which not only allow quantitative understanding of the device performances but also provide physical insight and guidelines for device optimizations. However, quantum transport simulations usually require solving repeatedly the Green’s function or the wave function of the whole device region with open boundary treatment, which are computationally cumbersome. Moreover, to overcome the short-channel effects, modern devices usually employ multi-gate structures that are three-dimensional, making the computation very challenging. It is the major target of this thesis to enhance the simulation efficiency by proposing several fast numerical algorithms. The other target is to apply these algorithms to study the physics and performances of some emerging electronic devices. First, an efficient method is implemented for real space simulations with the effective mass approximation. Based on the wave function approach, asymptotic waveform evaluation combined with a complex frequency hopping algorithm is successfully adopted to characterize electron conduction over a wide energy range. Good accuracy and efficiency are demonstrated by simulating several n-type multi-gate silicon FETs. This technique is valid for arbitrary potential distribution and device geometry, making it a powerful tool for studying n-type silicon nanowire (SiNW) FETs in the presence of charged impurity and surface roughness scattering. Second, a model order reduction (MOR) method is proposed for multiband simulation of nanowire structures. Employing three- or six-band k.p Hamiltonian, the non-equilibrium Green’s function (NEGF) equations are projected into a much smaller subspace constructed by sampling the Bloch modes of each cross-section layer. Together with special sampling schemes and Krylov subspace methods for solving the eigenmodes, large cross-section p-type SiNW FETs can be simulated. A novel device, junctionless FET, is then investigated. It is found that its doping density, channel orientation, and channel size need to be carefully optimized in order to outperform the classical inversion-mode FET. With a spurious band elimination process, the MOR method is subsequently extended to the eight-band k.p model, allowing simulation of band-to-band tunneling devices. In particular, tunneling FETs with indium arsenide (InAs) nanowire channel are studied, considering different channel orientations and configurations with source pockets. Results suggest that source pocket has no significant impact on the performances of the nanowire device due to its good electrostatic integrity. At last, improvements are made for open boundary treatment in atomistic simulations. The trick is to condense the Hamiltonian matrix of the periodic leads before calculating the surface Green’s function. It is very useful for treating leads with long unit cells. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

The primary goal of clinical trials is to collect enough scientific evidence for a new intervention. Despite the widespread use of equal randomization in clinical trials, response-adaptive randomization has attracted considerable interest in terms of ethical concerns. In this thesis, delayed response problems and innovative designs for cytostatic agents in oncology clinical trials are studied. There is typically a prerun of equal randomization before the implementation of response-adaptive randomization, while it is often not clear how many subjects are needed in this prephase, and in practice an arbitrary number of patients are allocated in this equal randomization stage. In addition, real-time response-adaptive randomization often requires patient response to be immediately available after the treatment, while clinical response, such as tumor shrinkage, may take a relatively long period of time to exhibit. In the first part of the thesis, a nonparametric fractional model and a parametric optimal allocation scheme are developed to tackle the common problem caused by delayed response. In addition, a two-stage procedure to achieve a balance between power and the number of responders is investigated, which is equipped with a likelihood ratio test before skewing the allocation probability toward a better treatment. The operating characteristics of the two-stage designs are evaluated through extensive simulation studies and an HIV clinical trial is used for illustration. Numerical results show that the proposed method satisfactorily resolves the issues involved in response-adaptive randomization and delayed response. In phase I clinical trials with cytostatic agents, toxicity endpoints, as well as efficacy effects, should be taken into consideration for identifying the optimal biological dose (OBD). In the second part of the thesis, a two-stage Bayesian mixture modeling approach is developed, which first locates the maximum tolerated dose (MTD) through a mixture of parametric and nonparametric models, and then determines the most efficacious dose using Bayesian adaptive randomization among multiple candidate models. In the first stage searching for the MTD, a beta-binomial model in conjunction with a probit model as a mixture modeling approach is studied, and decisions are made based on the model that better fits the toxicity data. The model fitting adequacy is measured by the deviance information criterion and the posterior model probability. In the second stage searching for the OBD, the assumption that efficacy monotonically increases with the dose is abandoned and, instead, all the possibilities that each dose could have the highest efficacy effect are enumerated so that the dose-efficacy curve can be increasing, decreasing, or umbrella-shape. Simulation studies show the advantages of the proposed mixture modeling approach for pinpointing the MTD and OBD, and demonstrate its satisfactory performance with cytostatic agents. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

abstract / Public Health / Doctoral / Doctor of Philosophy