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Full-Text Articles in Physical Sciences and Mathematics
Information Metrics For Predictive Modeling And Machine Learning, Kostantinos Gourgoulias
Information Metrics For Predictive Modeling And Machine Learning, Kostantinos Gourgoulias
Doctoral Dissertations
The ever-increasing complexity of the models used in predictive modeling and data science and their use for prediction and inference has made the development of tools for uncertainty quantification and model selection especially important. In this work, we seek to understand the various trade-offs associated with the simulation of stochastic systems. Some trade-offs are computational, e.g., execution time of an algorithm versus accuracy of simulation. Others are analytical: whether or not we are able to find tractable substitutes for quantities of interest, e.g., distributions, ergodic averages, etc. The first two chapters of this thesis deal with the study of the …
Inference Using Bhattacharyya Distance To Model Interaction Effects When The Number Of Predictors Far Exceeds The Sample Size, Sarah A. Janse
Inference Using Bhattacharyya Distance To Model Interaction Effects When The Number Of Predictors Far Exceeds The Sample Size, Sarah A. Janse
Theses and Dissertations--Statistics
In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that …
Approximate Statistical Solutions To The Forensic Identification Of Source Problem, Danica M. Ommen
Approximate Statistical Solutions To The Forensic Identification Of Source Problem, Danica M. Ommen
Electronic Theses and Dissertations
Currently in forensic science, the statistical methods for solving the identification of source problems are inherently subjective and generally ad-hoc. The formal Bayesian decision framework provides the most statistically rigorous foundation for these problems to date. However, computing a solution under this framework, which relies on a Bayes Factor, tends to be computationally intensive and highly sensitive to the subjective choice of prior distributions for the parameters. Therefore, this dissertation aims to develop statistical solutions to the forensic identification of source problems which are less subjective, but which retain the statistical rigor of the Bayesian solution. First, this dissertation focuses …