The Experts below are selected from a list of 946188 Experts worldwide ranked by ideXlab platform
Steven M. Quiring - One of the best experts on this subject based on the ideXlab platform.
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comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes
Risk Analysis, 2011Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk Management Resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. Language: en
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Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes
Risk Analysis, 2011Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk Management Resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.
Roshanak Nateghi - One of the best experts on this subject based on the ideXlab platform.
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comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes
Risk Analysis, 2011Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk Management Resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. Language: en
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Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes
Risk Analysis, 2011Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk Management Resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.
James A Shepperd - One of the best experts on this subject based on the ideXlab platform.
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information avoidance tendencies threat Management Resources and interest in genetic sequencing feedback
Annals of Behavioral Medicine, 2015Co-Authors: Jennifer M Taber, William M P Klein, Rebecca A Ferrer, Katie L Lewis, Peter R Harris, James A Shepperd, Leslie G BieseckerAbstract:Background Information avoidance is a defensive strategy that undermines receipt of potentially beneficial but threatening health information and may especially occur when threat Management Resources are unavailable.
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does lacking threat Management Resources increase information avoidance a multi sample multi method investigation
Journal of Research in Personality, 2014Co-Authors: Jennifer L Howell, Benjamin S Crosier, James A ShepperdAbstract:Are people who lack personal and interpersonal Resources are more likely to avoid learning potentially threatening information? We conducted four studies assessing three different populations (undergraduates, high school students, and a nationally-representative sample of adults), using a variety of measures and methods (e.g., single and multi-item self-report measures, a behavioral measure, social network analysis), across three information contexts (i.e., general health information, specific disease risk, socially-evaluative information). The consistent finding is that people who lack personal and interpersonal Resources to manage threat are more likely to avoid learning potentially-threatening information. The results indicate that personal and interpersonal Resources represent generalizable and robust predictors of information avoidance.
Leslie G Biesecker - One of the best experts on this subject based on the ideXlab platform.
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information avoidance tendencies threat Management Resources and interest in genetic sequencing feedback
Annals of Behavioral Medicine, 2015Co-Authors: Jennifer M Taber, William M P Klein, Rebecca A Ferrer, Katie L Lewis, Peter R Harris, James A Shepperd, Leslie G BieseckerAbstract:Background Information avoidance is a defensive strategy that undermines receipt of potentially beneficial but threatening health information and may especially occur when threat Management Resources are unavailable.
Seth D Guikema - One of the best experts on this subject based on the ideXlab platform.
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comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes
Risk Analysis, 2011Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk Management Resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. Language: en
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Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes
Risk Analysis, 2011Co-Authors: Roshanak Nateghi, Seth D Guikema, Steven M. QuiringAbstract:This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk Management Resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.
