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- Biosecurity (2)
- Absorptive Capacity (1)
- Bayesian Model Selection (1)
- Causal Ambiguity (1)
- Classification (1)
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- Electricity Demand (1)
- Forecasting and Time Series (1)
- K-S Test (1)
- Knowledge Transfer Difficulty (1)
- Linear Discriminant Analysis (1)
- Logistic Regression Analysis (1)
- Markov Chain Monte Carlo (1)
- Neural Networks (1)
- Outcome Ambiguity (1)
- Peak Load Forecasting (1)
- ROC Curves (1)
- Seemingly Unrelated Regression (1)
- Vector Autoregression (1)
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Modeling The Optimum Duration Of Antibiotic Prophylaxis In An Anthrax Outbreak, Ron Brookmeyer, Elizabeth Johnson, Robert Bollinger
Modeling The Optimum Duration Of Antibiotic Prophylaxis In An Anthrax Outbreak, Ron Brookmeyer, Elizabeth Johnson, Robert Bollinger
Ron Brookmeyer
A critical consideration in effective and measured public health responses to an outbreak of inhalational anthrax is the optimum duration of antibiotic prophylaxis. We develop a competing-risks model to address the duration of antibiotic prophylaxis and the incubation period that accounts for the risks of spore germination and spore clearance. The model predicts the incubation period distribution, which is confirmed by empirical data. The optimum duration of antibiotic prophylaxis depends critically on the dose of inhaled spores. At high doses, we show that exposed persons would need to remain on antibiotic prophylaxis for at least 4 months, and considerable morbidity …
Statistical Models And Bioterrorism: Application To The U.S. Anthrax Outbreak, Ron Brookmeyer, Natalie Blades
Statistical Models And Bioterrorism: Application To The U.S. Anthrax Outbreak, Ron Brookmeyer, Natalie Blades
Ron Brookmeyer
In the fall of 2001 an outbreak of inhalational anthrax occurred in the United States that was the result of bioterrorism. Letters contaminated with anthrax spores were sent through the postal system. In response to the outbreak, public health officials treated over 10,000 persons with antibiotic prophylaxis in the hopes of preventing further morbidity and mortality. No persons receiving the antibiotics subsequently developed disease. The question arises as to how many cases of disease may actually have been prevented by the public health intervention of antibiotic prophylaxis. A statistical model is developed to answer this question by relating to the …
Assessment Of Model Development Techniques And Evaluation Methods For Binary Classification In The Credit Industry, Satish Nargundkar, Jennifer Priestley
Assessment Of Model Development Techniques And Evaluation Methods For Binary Classification In The Credit Industry, Satish Nargundkar, Jennifer Priestley
Jennifer L. Priestley
We examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs associated with misclassification.
Absorptive Capacity, Causal Ambiguity And Outcome Ambiguity: The Network Effect And Knowledge Transfer Difficulty Among Four Network Forms, Subhashish Samaddar, Jennifer Priestley
Absorptive Capacity, Causal Ambiguity And Outcome Ambiguity: The Network Effect And Knowledge Transfer Difficulty Among Four Network Forms, Subhashish Samaddar, Jennifer Priestley
Jennifer L. Priestley
No abstract is currently available.
Bayesian Modelling And Forecasting Of Intra-Day Electricity Load, Remy Cottet, Michael Smith
Bayesian Modelling And Forecasting Of Intra-Day Electricity Load, Remy Cottet, Michael Smith
Michael Stanley Smith
With the advent of wholesale electricity markets there has been renewed focus on intra-day electricity load forecasting. This paper employs a multi-equation regression model with a diagonal first order stationary vector autoregresson (VAR) for modeling and forecasting intra-day electricity load. The correlation structure of the disturbances to the VAR and the appropriate subset of regressors are explored using Bayesian model selection methodology. The full spectrum of finite sample inference is obtained using a Bayesian Markov chain Monte Carlo sampling scheme. This includes the predictive distribution of load and the distribution of the time and level of daily peak load, something …