Department of Statistics
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Browsing Department of Statistics by Content Type "Presentation"
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- Academic LeadershipFricker, Ronald D. Jr. (2016-10)
- Assessing EARS’ Ability to Locally Detect the 2009 H1N1 PandemicFricker, Ronald D. Jr. (2011-05)
- Assessing the Methodology for Testing Body ArmorFricker, Ronald D. Jr.; Wilson, Alyson G. (2010-08-01)Conference presentation
- Biosurveillance: Detecting, Tracking, and Mitigating the Effects of Natural Disease and BioterrorismFricker, Ronald D. Jr.; Hanni, K. D. (2010-02)
- Data Science vs. Statistics: What's the Difference?Fricker, Ronald D. Jr. (2015-08-10)2015 Joint Statistical Meetings "Roundtable Discussion" presentation
- Detecting Anomalies in Space and Time with Application to BiosurveillanceFricker, Ronald D. Jr. (2008-08)
- Discussant for "Novel Contexts for SPC Methodology and Applications"Fricker, Ronald D. Jr. (2014-08)
- Educating Military Operations Research PractitionersFricker, Ronald D. Jr.; Dell, R. F. (2014-07)
- Improving Biosurveillance System PerformanceFricker, Ronald D. Jr. (2015-07)
- Improving Biosurveillance: Protecting People as Critical InfrastructureFricker, Ronald D. Jr. (2008-08)
- Improving the success of stream restoration practicesThompson, Theresa M.; Smith, Eric P. (2021-06-16)This research focused on 3 questions:
- Linking stream restoration success with watershed and design characteristics
- Design, project, and watershed factors that affect structure success
- Comparison of 1-D and 2-D HEC-RAS modeling for stream restoration design
- Model Robust Calibration: Method and Application to Electronically-Scanned Pressure TransducersWalker, Eric L.; Starnes, B. Alden; Birch, Jeffrey B.; Mays, James E. (American Institute of Aeronautics and Astronautics, 2010)This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed byMays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.
- Modeling Trust in Government: Empirically Assessing Mayer et al.'s Integrative Model of Organizational TrustFricker, Ronald D. Jr.; Kulzy, W. W.; Combs, D. J. Y. (2014-07)
- Optimizing a System of Threshold-based SensorsFricker, Ronald D. Jr.; Banschbach, D. (2007-11)
- Statistical Methods for BiosurveillanceFricker, Ronald D. Jr. (2007-10)
- Two Toys I've Been TestingFricker, Ronald D. Jr. (2009-05)
- Using the Repeated Two-Sample Rank Procedure for Detecting Anomalies in Space and TimeFricker, Ronald D. Jr. (2008-08)