Statistical Models Commons^™

Multidimensional Investigation Of Tennessee’S Urban Forest, Jillian L. Gorrell

Doctoral Dissertations

Preserving existing trees in urban areas and properly cultivating urban forest conservation and management opportunities is valuable to the ever-growing urban environment and necessary for creating optimal experiences and educational tools to meet the needs of increasing urban populations. This dissertation contains studies investigating several facets of the urban forest, including environmental effects of deforestation and urbanization, tree equity, and urban forest facility management and accessibility. Community education and outreach at arboreta about the importance of the tree canopy can help promote environmental stewardship. A digital questionnaire was electronically distributed to representatives of arboreta certified through the Tennessee Division of …

Modelling Cash Crop Growth In Tn, Spencer Weston

Chancellor’s Honors Program Projects

No abstract provided.

Impacts Of Climate Change On The Evolution Of The Electrical Grid, Melissa Ree Allen

Doctoral Dissertations

Maintaining interdependent infrastructures exposed to a changing climate requires understanding 1) the local impact on power assets; 2) how the infrastructure will evolve as the demand for infrastructure changes location and volume and; 3) what vulnerabilities are introduced by these changing infrastructure topologies. This dissertation attempts to develop a methodology that will a) downscale the climate direct effect on the infrastructure; b) allow population to redistribute in response to increasing extreme events that will increase under climate impacts; and c) project new distributions of electricity demand in the mid-21^st century.

The research was structured in three parts. The first …

Automating Large-Scale Simulation Calibration To Real-World Sensor Data, Richard Everett Edwards

Doctoral Dissertations

Many key decisions and design policies are made using sophisticated computer simulations. However, these sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. This dissertation's goal is to address these simulation deficiencies by presenting a general automated process for tuning simulation inputs such that simulation output matches real world measured data. The automated process involves the following key components -- 1) Identify a model that accurately estimates the real world simulation calibration target from measured sensor data; 2) Identify …

Identifying The Spatial Distribution Of Three Plethodontid Salamanders In Great Smoky Mountains National Park Using Two Habitat Modeling Methods, Matthew Stephen Kookogey

Masters Theses

The main objective was to create habitat models of three plethodontid salamander species (Desmognathus conanti, D. ocoee, and Plethodon jordani) in GSMNP. To investigate the relationships between salamanders and their habitats, I used three models—logistic regression with use-availability sampling, logistic regression with case-control sampling, and Mahalanobis distance (D²)—for each species to gain a robust view of the relationships. The secondary objective was to compare the different modeling methods within and across the three species. Elevation was the dominant variable for all three species.

D² for D. conanti predicted low elevations, close proximity …

Mixture Model Cluster Analysis Under Different Covariance Structures Using Information Complexity, Bahar Erar

Masters Theses

In this thesis, a mixture-model cluster analysis technique under different covariance structures of the component densities is developed and presented, to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data sets to achieve flexibility in currently practiced cluster analysis techniques. Two approaches to parameter estimation are considered and compared; one using the Expectation-Maximization (EM) algorithm and another following a Bayesian framework using the Gibbs sampler. We develop and score several forms of the ICOMP criterion of Bozdogan (1994, 2004) as our fitness function; to choose the number …

Mixture Of Factor Analyzers With Information Criteria And The Genetic Algorithm, Esra Turan

Doctoral Dissertations

In this dissertation, we have developed and combined several statistical techniques in Bayesian factor analysis (BAYFA) and mixture of factor analyzers (MFA) to overcome the shortcoming of these existing methods. Information Criteria are brought into the context of the BAYFA model as a decision rule for choosing the number of factors m along with the Press and Shigemasu method, Gibbs Sampling and Iterated Conditional Modes deterministic optimization. Because of sensitivity of BAYFA on the prior information of the factor pattern structure, the prior factor pattern structure is learned directly from the given sample observations data adaptively using Sparse Root algorithm. …