Physical Sciences and Mathematics Commons^™

Economic Challenges Facing Kentucky’S Electricity Generation Under Greenhouse Gas Constraints, Energy And Environment Cabinet, Commonwealth Of Kentucky, Department Of Statistics, University Of Kentucky, Center For Applied Energy Research, University Of Kentucky, Pacific Northwest National Laboratory

Statistics Reports

From the preface:

For the Energy and Environment Cabinet (EEC), which has primacy in administering most federal environmental laws and regulations at the state level, we have to understand the implications of what is arguably one of the most challenging issues to confront us—greenhouse gas (GHG) emissions and their impact on climate change. Efforts to reduce GHG or carbon dioxide (CO₂) emissions have moved beyond the point of discussion at the national level, and the United States Supreme Court has ruled that the U.S. Environmental Protection Agency (EPA) has the authority to regulate GHG emissions. Furthermore, while public …

Approximate Techniques In Solving Optimal Camera Placement Problems, Jian Zhao, Ruriko Yoshida, Sen-Ching Samson Cheung, David Haws

Statistics Faculty Publications

While the theoretical foundation of the optimal camera placement problem has been studied for decades, its practical implementation has recently attracted significant research interest due to the increasing popularity of visual sensor networks. The most flexible formulation of finding the optimal camera placement is based on a binary integer programming (BIP) problem. Despite the flexibility, most of the resulting BIP problems are NP-hard and any such formulations of reasonable size are not amenable to exact solutions. There exists a myriad of approximate algorithms for BIP problems, but their applications, efficiency, and scalability in solving camera placement are poorly understood. Thus, …

Risk Score Modeling Of Multiple Gene To Gene Interactions Using Aggregated-Multifactor Dimensionality Reduction, Hongying Dai, Richard J. Charnigo, Mara L. Becker, J. Steven Leeder, Alison A. Motsinger-Reif

Statistics Faculty Publications

BACKGROUND: Multifactor Dimensionality Reduction (MDR) has been widely applied to detect gene-gene (GxG) interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk) from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account.

RESULTS: We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification …

A Neural Network Model To Translate Brain Developmental Events Across Mammalian Species, Radhakrishnan Nagarajan, Jeffrey N. Jonkman

Biostatistics Faculty Publications

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially useful since the distribution of the event timing data is skewed with a majority of events documented only across a few selected species. The present study investigates the choice of single hidden layer feed-forward neural networks (FFNN) for predicting the unknown events from the empirical data. A …