This dissertation focuses on three research projects: 1) construction of simultaneous prediction intervals/bounds for at least k out of m future observations; 2) semi-parametric degradation model for accelerated destructive degradation test (ADDT) data; and 3) spatial variable selection and application to Lyme disease data in Virginia. Followed by the general introduction in Chapter 1, the rest of the dissertation consists of three main chapters. Chapter 2 presents the construction of two-sided simultaneous prediction intervals (SPIs) or one-sided simultaneous prediction bounds (SPBs) to contain at least k out of m future observations, based on complete or right censored data from (log)-location-scale family of distributions. SPI/SPB calculated by the proposed procedure has exact coverage probability for complete and Type II censored data. In Type I censoring case, it has asymptotically correct coverage probability and reasonably good results for small samples. The proposed procedures can be extended to multiply-censored data or randomly censored data. Chapter 3 focuses on the analysis of ADDT data. We use a general degradation path model with correlated covariance structure to describe ADDT data. Monotone B-splines are used to modeling the underlying degradation process. A likelihood based iterative procedure for parameter estimation is developed. The confidence intervals of parameters are calculated using the nonparametric bootstrap procedure. Both simulated data and real datasets are used to compare the semi-parametric model with the existing parametric models. Chapter 4 studies the Lyme disease emergence in Virginia. The objective is to find important environmental and demographical covariates that are associated with Lyme disease emergence. To address the high-dimentional integral problem in the loglikelihood function, we consider the penalized quasi loglikelihood and the approximated loglikelihood based on Laplace approximation. We impose the adaptive elastic net penalty to obtain sparse estimation of parameters and thus to achieve variable selection of important variables. The proposed methods are investigated in simulation studies. We also apply the proposed methods to Lyme disease data in Virginia. Finally, Chapter 5 contains general conclusions and discussions for future work. / Ph. D.

Demand for energy-saving variable speed drives in low-cost, high-volume appliances has increased due to energy and environmental concerns and hence the need to comply with new regulations. Switched reluctance motor (SRMs) have been considered by many as attractive alternatives for brush commutated motors or permanent magnet brushless dc motors (PMBDCMs) in such cost-sensitive applications. The SRMs' unique features such as simple and fault-tolerant structure and unidirectional flow of their phase currents endow them with the possibility of various configurations on both machine and converter topologies for different applications. In the present study, three different variable-speed motor drive systems are proposed, studied, and implemented for their deployment in low-cost, high-volume applications with the power rating of 1.5kW or less. Two different two-phase SRMs and three different power converters are employed to realize three different low-cost drive systems. The first drive system is realized using a novel converter requiring only a single-controllable switch and an asymmetric two-phase 8/4 SRM capable of self-starting and four-quadrant operation. The second drive system is realized using another novel converter requiring two controllable switches, that way to achieve better control and utilization of the asymmetric 8/4 motor. The target applications for both drive systems are low power, low performance drives such as fans, hand tools, small appliances, etc. The third system is realized using a high-speed two-phase 4/2 SRM and a split ac source converter, which is designed for high-speed applications such as vacuum cleaners, ultracentrifuges, etc. The control and design aspects for each drive system are studied. Selection of optimal firing angles and optimal number of winding turns are also investigated. All of the drive systems are first demonstrated on the position sensor-based speed-control scheme. To make the drive system even more cost-competitive, operation without the position sensor using the novel parameter insensitive sensorless control scheme is proposed and implemented. Concept, analysis, simulation, and experimental verification of the proposed sensorless scheme are discussed in detail. / Ph. D.

Spectral reflectance-based, remote sensing technology has been used to adjust in-season nitrogen (N) fertilizer rates for wheat to account for spatial variability in grain yield potential at a sub-meter resolution. The objective of this study was to examine the relationships among spectral reflectance indices, corn tissue N content, chlorophyll measurements, plant size and spacing measurements, and grain yield to develop a similar strategy for variable-rate N management in corn. Irrigated and non-irrigated studies were conducted during the 2002 and 2003 growing seasons in eastern Virginia. Plots were treated with various rates of preplant, starter, and sidedress N fertilizer to establish a wide range of grain yield potential. Spectral measurements, tissue N, chlorophyll measurements, and plant physical measurements were collected at growth stages V6, V8, and V10. At maturity, grain yield was determined and correlated with in-season data and optimum N rate to calibrate in-season, variable-rate N fertilization strategies. Results from these studies indicate that spectral reflectance is well correlated with plant N uptake and chlorophyll meter readings and can also be correlated with final grain yield. These relationships may be used to develop a model to predict in-season, variable N application rates for corn production at a sub-meter resolution. / Master of Science

As data collection techniques improve, oftentimes the number of covariates exceeds the number of observations. When this happens, regression models become oversaturated and, thus, inestimable. Many classical and Bayesian techniques have been designed to combat this difficulty, with various means of combating the oversaturation. However, these techniques can be tricky to implement well, difficult to interpret, and unstable. What is proposed is a technique that takes advantage of the natural clustering of variables that can often be found in biological and ecological datasets known as the omics datasests. Generally speaking, omics datasets attempt to classify host species structure or function by characterizing a group of biological molecules, such as genes (Genomics), the proteins (Proteomics), and metabolites (Metabolomics). By clustering the covariates and regressing on a single value for each cluster, the model becomes both estimable and stable. In addition, the technique can account for the variability within each cluster, allow for the inclusion of expert judgment, and provide a probability of inclusion for each cluster. / Ph. D.

In this thesis we propose a novel modification to Bayesian decision tree methods. We provide a historical survey of the statistics and computer science research in decision trees. Our approach facilitates covariate selection explicitly in the model, something not present in previous research. We define a transformation that allows us to use priors from linear models to facilitate covariate selection in decision trees. Using this transform, we modify many common approaches to variable selection in the linear model and bring these methods to bear on the problem of explicit covariate selection in decision tree models. We also provide theoretical guidelines, including a theorem, which gives necessary and sufficient conditions for consistency of decision trees in infinite dimensional spaces. Our examples and case studies use both simulated and real data cases with moderate to large numbers of covariates. The examples support the claim that our approach is to be preferred in large dimensional datasets. Moreover, our approach shown here has, as a special case, the model known as Bayesian CART. / Ph. D.

Nitrate (NO⁻3) export can vary widely among forested watersheds with similar nitrogen loading, geology, and vegetation, which suggests the importance of understanding differing internal retention mechanisms. Transport should be studied at the hillslope scale because the hillslope is the smallest unit with spatial and temporal resolution to reflect many relevant NO⁻3 retention and transport (flow-generation) processes, and headwater forested watersheds are largely comprised of sections of hillslopes. I conducted two experiments to elucidate subsurface flow dynamics and NO⁻3 transport and retention mechanisms on a constructed experimental hillslope model. In the first experiment, I tested whether decadal pedogenetic changes in soil properties in the experimental hillslope used by Hewlett and Hibbert (1963) would lead to changes in recession flow. I repeated (twice) their seminal experiment, whose results led to the development of the Variable Source Area paradigm, by also saturating, covering, and allowing the experimental hillslope to drain until it no longer yielded water. In the historical experiment there was fast drainage for 1.5 d, followed by slow drainage for ~140 d, which led the authors to conclude that recession flow in unsaturated soil could sustain baseflow throughout droughts. This long, slow drainage period was not reproduced in my experiments. Shapes of the drainage curves in my experiments were similar to the historical curve, but slow drainage was truncated, ending after 17 and 12 d, due likely to a leak in the boundary conditions, rather than to pedogenetic changes since the historical experiment. Leakage to bedrock, analogous to the leak in the hillslope model, is a commonly observed phenomenon and this study highlights how that can reduce drainage duration and the contribution of moisture from soils to support baseflow. In the second experiment, I tested whether movement of NO⁻3, which is considered a mobile ion, would be delayed relative to movement of water through a hillslope. I added concentrated pulses of ¹⁵NO⁻3 and a conservative tracer (²H₂O) on the same experimental hillslope, which was devegetated and irrigated at hydrologic steady state. Retention of the ¹⁵NO⁻3 tracer was high in the soil surface (0–10 cm) layer directly where the tracer was added. The portion of the ¹⁵NO⁻3 tracer that passed through this surface layer was further retained/removed in deeper soil. The reduction in the peaks in δ¹⁵N breakthrough was an order of magnitude larger than in δ₂H breakthrough at the outlet 5 m downslope of the tracer addition. The peaks in δ¹⁵N were also delayed relative to the peaks in δ₂H by 1, 6, 9 and 18.5 d for slope distances of 0, 2, 4, and 5 m, respectively, from tracer addition to the outlet. The excess mass of ¹⁵NO⁻3 recovered at the outlet was less than 3% of the original tracer mass injected. Nitrification and denitrification were estimated to be roughly 1:1 and were large fluxes relative to lateral transport into and out of the riparian zone. This tracer experiment shows that bedrock leakage, coupled with multiple retention/removal mechanisms can significantly delay export of added NO⁻3 with implications of additional NO⁻3 sink strength at the watershed scale. / Ph. D. / Nitrate (NO₃⁻) export can vary widely among forested watersheds with similar nitrogen loading, geology, and vegetation, which suggests the importance of understanding differing internal process mechanisms. I conducted two experiments to illustrate how water and NO₃⁻ moved on a constructed hillslope model. In the first experiment, I quantified differences in soil properties in the hillslope model used by Hewlett and Hibbert (1963). Then I repeated (twice) the seminal drainage experiment described in Hewlett and Hibbert (1963). The same hillslope (21.8°; 40%) was wetted up, covered, and allowed to drain until water stopped exiting at the outlet. In the historical experiment there was fast drainage for 1.5 d, followed by slow drainage for ~140 d, which led the authors to hypothesize that slow drainage in surface soil could continually contribute water to streams even during droughts. This long, slow drainage period was not reproduced in my experiments. Drainage was similar at the beginning of drainage between my experiments and the historical experiment, but in my experiment the slow drainage ended earlier (after 17 and 12 d) due likely to a leak in the constructed hillslope model, rather than to significant changes that occurred in the soil itself since the original experiment. This leak in the hillslope model is similar to leakage to bedrock, which is commonly observed in natural hillslopes. In the second experiment, I tested whether NO₃⁻ and water would move through a hillslope at the same rate. I added concentrated pulses of NO₃⁻ (as ¹⁵NO₃⁻ and water (as ²H₂O) on the same devegetated experimental hillslope. Retention of the ¹⁵NO₃⁻ tracer was high in the surface (0–10 cm) where the tracer was added, with little change in the immediately surrounding soil, despite high rates of water input immediately after tracer addition and throughout the experiment. The portion of the ¹⁵NO₃⁻ tracer that passed through the surface layer was further processed by microbes in deeper soil as it traveled downslope. This body of work shows that bedrock leakage, coupled with multiple retention mechanisms throughout the soil profile, can significantly delay export of added NO₃⁻ at the watershed scale.

In this paper, we begin in Chapter 1 by giving a brief overview of the history of fractal geometry, focusing on six of the most important mathematicians in this field. Chapter 2 explains the main definitions needed for the remainder of the paper. In Chapter 3, we clarify the process of creating fractals from iterated function systems. Chapter 4 consists of an examination of the properties of traditional fractals. Next, in Chapter 5, we examine the newly discovered V-variable fractals and their properties. Finally we consider applications and future research in the field of fractal geometry.

This paper examines the response of Swedish local governments to a targeted intergovernmental stimulus grant aimed at increasing staffing levels in elderly care. The focus is on two key outcomes: municipal elderly care personnel costs relative to total municipal costs and the number of full-time employees in elderly care per elderly user. An OLS regression based on panel data between 2011 and 2018 initially estimates the grant’s spending effects. An instrumental variable (IV) model is then employed to address potential endogeneity, utilizing an update in the grant allocation formula. Both the OLS and IV estimates suggest that the stimulus grant has no discernible effect on the ratio of elderly care personnel costs to total municipal spending. Furthermore, the IV results show insignificant short-run effects on full-time employment in elderly care. However, significant increases are observed three years after the allocation formula update. The overall effects confirm standard economic grant theory predicting how non-matching targeted grants only contribute to an income effect.

Geomantic culture, deeply rooted in China's rich traditions, stands out as one of the most captivating and mysterious aspects of the country's heritage. In thousands of years, this enigmatic blend of metaphysics and geomancy has significantly shaped the lives of the Chinese people. Its historical success in predicting and judging risks has made it a valuable tool for investors in the field of real estate. In this case, research on the influence of risks of metaphysics and geomancy becomes necessarily important to facilitate investors to own relatively feasible tools when participating in economic activities. In this dissertation, I collect property transaction data in a district of city of Beijing in China in August 2023, and examine how various geomantic characteristics affect property prices. I identify sixteen indicators of geomantic factors, among which four are variables of community characteristics (location, shape, main entrance and road), four are building characteristics (position, orientation, floor and house number), and eight are internal characteristics of residences (house type, layout, daylighting, ventilation, color scheme, five elements, door and window). Quantification of these 16 characteristic variables can be divided as quantitative quantification and qualitative quantification, and the method to quantify indicators in accordance with the actual situation is an innovative content of indicator quantification of this paper. Employing multiple linear regressions, I find that 10 out of the 16 characteristic variables, including location, shape, main entrance, road, position, orientation, floor, house number, house type, and layout, are significantly related to property prices. I also compare the strength of the effect of each geomantic characteristic variable on property prices through standardized regression. Community location of the property has the greatest influence on prices. a building's position is at the second most important factor. House number and layout seem having the least effect on property transaction prices. My research suggests that homeowners in China care about the geomantic characteristics of their property, hence are willing to pay a higher price for more favorable geomantic characteristics. This study also offers useful guidance for real estate developers how to maximize profits through improving geomantic characteristics of their property development. / Global Finance

The current investigation is framed within variationist sociolinguistics, an area of study that examines how language varies and is influenced by linguistic and extralinguistic factors. Variationist research has reported that the use of verbal moods (the subjunctive and indicative) in Spanish is variable. One of the reasons for this variation appears to be a centuries-long process of language change, during which the subjunctive mood seemed to show a decline in favor of the indicative mood. Considering this gradual process of change, various investigations have examined how sociolinguistic factors condition variability in Spanish, focusing their analyses on different Spanish-speaking regions. To contribute to the understanding of mood variation in Spanish, this study explored a range of sociolinguistic factors across three Spanish-speaking regions (Rosario, Argentina; Barcelona, Spain; and Seville, Spain) by means of a conceptual replication of Gudmestad (2021), which studied variable mood use in Spanish across three regions (Quito, Ecuador; Mérida, Mexico; and Seville, Spain). The current study's participant pool (N = 107) consisted of Spanish speakers—men and women over 18 years old—residing in the three aforementioned metropolitan areas, who had lived in the same location for at least 15 years at the moment of the data collection. The data were collected via a written clause-elicitation task, with the purpose of having enough verbal-mood contexts to analyze, and a background questionnaire with basic demographic information. The results suggested that there was geographical variation of mood use, which reinforced the original study's findings on regional variation of mood use. Like Gudmestad (2021), the patterns of verbal moods with individual governors (e.g., preferir que 'to prefer that'), semantic category, and time reference diverged across the regions. The examination of gender and individual participants also pointed to a possible connection between these factors and variable mood use across regions. These findings indicate that the envelopes of variation of mood use may be diverse across the regions under investigation, which suggests that different geographical regions may have slightly distinct grammars. / Master of Arts / The current investigation is framed within variationist sociolinguistics, an area of study that examines how language varies, and which factors may condition this variation. Research within this field has found that Spanish speakers use subjunctive and indicative moods in a variable way. This variability in mood use may be connected to a long process of language change, over which speakers seem to be using the indicative mood in instances in which, in the past, they would have used the subjunctive mood. Taking this into account, various investigations have studied how the use of verbal moods in Spanish varies by analyzing different locations where Spanish is spoken. To contribute to this body of knowledge, the present investigation studied variable mood use in three regions (Rosario, Argentina; Barcelona, Spain; and Seville, Spain), by partially replicating an original investigation conducted by Gudmestad (2021) on variable mood use in Spanish across the regions of Quito, Ecuador; Mérida, Mexico; and Seville, Spain. Participants of the replication study (a total of 107) were three geographical groups of Spanish speakers. They were men and women over 18 years old who were residing in the three aforementioned urban areas and who had lived in the same location for at least 15 years. The participants completed a story with blanks that elicited the use of verbal moods and a questionnaire with basic demographic information. The findings indicated that Spanish speakers may use the subjunctive and indicative moods variably, according to different linguistic contexts and extralinguistic characteristics. Regarding geographical variation, the results showed that mood use was variable across the different locations analyzed, which reinforced the original study's findings on the presence of regional variation of mood use in Spanish.