S
Seth D. Guikema
Researcher at University of Michigan
Publications - 189
Citations - 5680
Seth D. Guikema is an academic researcher from University of Michigan. The author has contributed to research in topics: Risk analysis & Poison control. The author has an hindex of 36, co-authored 182 publications receiving 4465 citations. Previous affiliations of Seth D. Guikema include Stanford University & University of Stavanger.
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Probabilistic Modeling of Terrorist Threats: A Systems Analysis Approach to Setting Priorities Among Countermeasures
TL;DR: This paper presents a model for setting priorities among threats and among countermeasures, based on probabilistic risk analysis, decision analysis, and elements of game theory, and uses the rational decision analysis model in a descriptive mode on the terrorist side and in a prescriptive mode for the United States.
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Application of the Conway–Maxwell–Poisson generalized linear model for analyzing motor vehicle crashes
TL;DR: The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance.
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Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
TL;DR: Multiple regression and machine learning approaches are used to simulate monthly streamflow in five highly seasonal rivers in the highlands of Ethiopia and compare their performance in terms of predictive accuracy, error structure and bias, model interpretability, and uncertainty when faced with extreme climate conditions.
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Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.
TL;DR: The out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004 are compared and BART yields the best prediction accuracy and it is possible to predict outage durations with reasonable accuracy.
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A flexible count data regression model for risk analysis.
TL;DR: The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.