Scholarly Works, Statistics

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Research articles, presentations, and other scholarship

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    A Note on the General Likelihood Measure of Overlap
    Smith, Eric P. (Ecological Society of America, 1984)
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    Attrition Models of the Ardennes Campaign
    Fricker, Ronald D. Jr. (1998)
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    Bayesian QTL mapping using skewed Student-tdistributions
    von Rohr, Peter; Hoeschele, Ina (2002-01-15)
    In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.
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    A Model to Predict the Impact of Specification Changes on Chloride-Induced Corrosion Service Life of Virginia Bridge Decks
    Trevor J. Kirkpatrick; Richard E. Weyers; Anderson-Cook, Christine M.; Michael M. Sprinkel; Michael C. Brown (Virginia Center for Transportation Innovation and Research, 2002-10)
    A model to determine the time to first repair and subsequent rehabilitation of concrete bridge decks exposed to chloride deicer salts that recognizes and incorporates the statistical nature of factors affecting the corrosion process is developed. The model expands on an existing deterministic model by using statistical computing techniques, including resampling techniques such as the parametric and simple bootstrap. Emphasis was placed on the diffusion portion of the diffusion-cracking model, but advances can be readily included for the time for corrosion deterioration after corrosion initiation. Data collected from ten bridge decks built in Virginia between 1981 and 1994 were used to model the surface chloride concentration, apparent diffusion coefficient, and clear cover depth. Several ranges of the chloride corrosion initiation concentration, as determined from the available literature, were investigated. The time to first repair and subsequent rehabilitation predicted by the stochastic model is shorter than the time to first repair and subsequent rehabilitation predicted by the deterministic model. The stochastic model is believed to more accurately reflect the true nature of bridge deck deterioration because it takes into account the fact that data for each of the parameters affecting chloride diffusion and corrosion initiation are not necessarily normally distributed. The model was validated by comparison of projected service lives of bridge decks built from 1981 to 1994 derived from the model to historical service life data for 129 bridge decks built in Virginia between 1968 and 1972. The time to rehabilitation predicted for the set of bridge decks built between 1981 and 1994 by the stochastic model was approximately 13 years longer than the normalized time to rehabilitation projected for the bridge decks built between 1968 and 1972 using historical data. The time to first repair and rehabilitation predicted by the probabilistic method more closely matches that of historical data than the time to first repair and rehabilitation predicted by the average value solution. The additional service life expected for the set of bridges built between 1981 and 1994 over those constructed from 1968 to 1972 can be attributed to the decrease in w/c ratio from 0.47 to 0.45 and slight increase in as-built cover depth from approximately 50 mm (2 in.) to 63.5 to 76 mm (2.5 to 3.0 in.).
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    Game Theory in an Age of Terrorism: How Can Statisticians Contribute?
    Fricker, Ronald D. Jr. (Springer, 2006)
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    Evaluating Statistical Methods for Syndromic Surveillance
    Fricker, Ronald D. Jr.; Stoto, Michael A.; Jain, Arvind; Diamond, Alexis; Davies-Cole, John O.; Glymph, Chevelle; Kidane, Gebreyesus; Lum, Garrett; Jones, LaVerne; Dehan, Kerda; Yuan, Christine (Springer, 2006)
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    Statistical Methods for Biosurveillance
    Fricker, Ronald D. Jr. (2007-10)
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    Optimizing a System of Threshold-based Sensors
    Fricker, Ronald D. Jr.; Banschbach, D. (2007-11)
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    Die-off of E. coli and enterococci in dairy cowpats
    Soupir, M. L.; Mostaghimi, Saied; Lou, J. (American Society of Agricultural and Biological Engineers, 2008)
    E. coli and enterococci re-growth and decay patterns in cowpats applied to pasturelands were monitored during the spring, summer fall, and winter First-order approximations were used to determine die-off rate coefficients and decimal reduction times (D-values). Higher-order approximations and weather parameters were evaluated by multiple regression analysis to identify environmental parameters impacting in-field E. coli and enterococci decay. First-order kinetics approximated E. coli and enterococci decay rates with regression coefficients ranging from 0.70 to 0.90. Die-off rate constants were greatest in cowpats applied to pasture during late winter and monitored into summer months for E. coli (k = 0.0995 d(-1)) and applied to the field during the summer and monitored until December for enterococci (k = 0.0978 d(-1)). Decay rates were lowest in cowpats applied to the pasture during the fall and monitored over the winter (k = 0.0581 d(-1) for E. coli, and k = 0.0557 d(-1) for enterococci). Higher-order approximations and the addition of weather variables improved regression coefficients to values ranging from 0.82 to 0.96. Statistically significant variables used in the models for predicting bacterial decay included temperature, solar radiation, rainfall, and relative humidity. Die-off rate coefficients previously reported in the literature are usually the result of laboratory-based studies and are generally higher than the field-based seasonal die-off rate coefficients presented here. To improve predictions of in-field E. coli and enterococci concentrations, this study recommends that higher-order approximations and additional parameters such as weather variables are necessary to better capture re-growth and die-off trends over extended periods of time.
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    Biosurveillance: Detecting, Tracking, and Mitigating the Effects of Natural Disease and Bioterrorism
    Fricker, Ronald D. Jr. (Wiley, 2008)
    Biosurveillance is the regular collection, analysis, and interpretation of health and health-related data for indicators of diseases and other outbreaks by public health organizations. Motivated by the threat of bioterrorism, biosurveillance systems are being developed and implemented around the world. The goal of these systems has been expanded to include both early event detection and situational awareness, so that the focus is not simply on detection, but also on response and consequence management. Whether they are useful for detecting bioterrorism or not, there seems to be consensus that these biosurveillance systems are likely to be useful for detecting and responding to natural disease outbreaks such as seasonal and pandemic flu, and thus they have the potential to significantly advance and modernize the practice of public health surveillance.
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    Syndromic Surveillance
    Fricker, Ronald D. Jr. (Wiley, 2008)
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    A Spatio-temporal Methodology for Real-time Biosurveillance
    Fricker, Ronald D. Jr.; Chang, JT (2008)
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    Directionally Sensitive MCUSUM and MEWMA Procedures with Application to Biosurveillance
    Fricker, Ronald D. Jr.; Knitt, MC; Hu, CX (2008)
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    Comparing syndromic surveillance detection methods: EARS' versus a CUSUM-based methodology.
    Fricker, Ronald D. Jr.; Hegler, B. L.; Dunfee, D. A. (2008-07-30)
    This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed significantly better than the EARS' methods across all of the scenarios we evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle), daily effects, and various types and levels of random daily variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.
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    Assessing the Effects of Individual Augmentation (IA) on Active Component Navy Enlisted and Officer Retention
    Fricker, Ronald D. Jr.; Buttrey, Samuel E. (Naval Postgraduate School, 2008-08)
    This report summarizes the results of an analysis of whether individual augmentation (IA) deployment affects retention rates for Navy enlisted personnel and junior officers. The analysis compared retention rates between those personnel who have been deployed via IA to equivalent cohorts of Navy personnel who have not been on an IA deployment. Retention rates were compared in three different ways: aggregate comparisons, comparisons by individual demographic categories, and comparisons based on standard statistical modeling techniques (logistic regression), in order to simultaneously control for all the demographic and other observable characteristics. Overall, the analysis found little evidence that IA deployment is hurting retention rates among those who have experienced one or more IA deployments. In fact, in almost all of the comparisons, the retention rates of those who have had one or more IA deployments were higher than the retention rates of their Navy colleagues who have only been on conventional Navy deployments. The only categories where lower retention rates were definitively identified were for E-3s and E-4s, though the decrease in retention rates was only about one percent.
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