Modeling Precheck Parallel Screening Process in the Face of Strategic Applicants with Incomplete Information and Screening Errors

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In security check systems, tighter screening processes increase the security level, but also cause more congestion, which could cause longer wait times. Having to deal with more congestion in lines could also cause issues for the screeners. The Transportation Security Administration (TSA) Precheck Program was introduced to create fast lanes in airports with the goal of expediting passengers who the TSA does not deem to be threats. In this lane, the TSA allows passengers to enjoy fewer restrictions in order to speed up the screening time. Motivated by the TSA Precheck Program, we study parallel queueing imperfect screening systems, where the potential normal and adversary participants/applicants decide whether to apply to the Precheck Program or not. The approved participants would be assigned to a faster screening channel based on a screening policy determined by an approver, who balances the concerns of safety of the passengers and congestion of the lines. There exist three types of optimal normal applicant’s application strategy, which depend on whether the marginal payoff is negative or positive, or whether the marginal benefit equals the marginal cost. An adversary applicant would not apply when the screening policy is sufficiently large or the number of utilized benefits is sufficiently small. The basic model is extended by considering (1) applicants’ parameters to follow different distributions and (2) applicants to have risk levels, where the approver determines the threshold value needed to qualify for Precheck. This article integrates game theory and queueing theory to study the optimal screening policy and provides some insights to imperfect parallel queueing screening systems.

May 29, 2017 at 07:02PM

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from Cen Song, Jun Zhuang

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Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams

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Risk research has theorized a number of mechanisms that might trigger, prolong, or potentially alleviate individuals’ distress following terrorist attacks. These mechanisms are difficult to examine in a single study, however, because the social conditions of terrorist attacks are difficult to simulate in laboratory experiments and appropriate preattack baselines are difficult to establish with surveys. To address this challenge, we propose the use of computational focus groups and a novel analysis framework to analyze a social media stream that archives user history and location. The approach uses time-stamped behavior to quantify an individual’s preattack behavior after an attack has occurred, enabling the assessment of time-specific changes in the intensity and duration of an individual’s distress, as well as the assessment of individual and social-level covariates. To exemplify the methodology, we collected over 18 million tweets from 15,509 users located in Paris on November 13, 2015, and measured the degree to which they expressed anxiety, anger, and sadness after the attacks. The analysis resulted in findings that would be difficult to observe through other methods, such as that news media exposure had competing, time-dependent effects on anxiety, and that gender dynamics are complicated by baseline behavior. Opportunities for integrating computational focus group analysis with traditional methods are discussed.

May 29, 2017 at 07:02PM

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from Yu-Ru Lin, Drew Margolin, Xidao Wen

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Incremental Sampling Methodology: Applications for Background Screening Assessments

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This article presents the findings from a numerical simulation study that was conducted to evaluate the performance of alternative statistical analysis methods for background screening assessments when data sets are generated with incremental sampling methods (ISMs). A wide range of background and site conditions are represented in order to test different ISM sampling designs. Both hypothesis tests and upper tolerance limit (UTL) screening methods were implemented following U.S. Environmental Protection Agency (USEPA) guidance for specifying error rates. The simulations show that hypothesis testing using two-sample t-tests can meet standard performance criteria under a wide range of conditions, even with relatively small sample sizes. Key factors that affect the performance include unequal population variances and small absolute differences in population means. UTL methods are generally not recommended due to conceptual limitations in the technique when applied to ISM data sets from single decision units and due to insufficient power given standard statistical sample sizes from ISM.

May 29, 2017 at 07:02PM

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from Penelope S. Pooler, Philip E. Goodrum, Deana Crumbling, Leah D. Stuchal, Stephen M. Roberts

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Bias-Corrected Estimation in Continuous Sampling Plans

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Continuous sampling plans (CSPs) are algorithms used for monitoring and maintaining the quality of a production line. Although considerable work has been done on the development of CSPs, to our knowledge, there has been no corresponding effort in developing estimators with good statistical properties for data arising from a CSP inspection process. For example, information about the failure rate of the process will affect the management of the process, both in terms of selecting appropriate CSP parameters to keep the failure rate after inspection at a suitable level, and in terms of policy, for example, whether the process should be completely inspected, or shut down. The motivation for this exercise was developing sampling protocols for Australia’s Department of Agriculture and Water Resources for monitoring the biosecurity compliance of incoming goods at international borders. In this study, we show that maximum likelihood estimation of the failure rate under a sampling scheme can be biased depending on when estimation is performed, and we provide explicit expressions for the main contribution of the bias under various CSPs. We then construct bias-corrected estimators and confidence intervals, and evaluate their performance in a numerical study.

May 29, 2017 at 07:02PM

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from Geoffrey Decrouez, Andrew Robinson

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Bias-Corrected Estimation in Continuous Sampling Plans

http://ift.tt/eA8V8J

Continuous sampling plans (CSPs) are algorithms used for monitoring and maintaining the quality of a production line. Although considerable work has been done on the development of CSPs, to our knowledge, there has been no corresponding effort in developing estimators with good statistical properties for data arising from a CSP inspection process. For example, information about the failure rate of the process will affect the management of the process, both in terms of selecting appropriate CSP parameters to keep the failure rate after inspection at a suitable level, and in terms of policy, for example, whether the process should be completely inspected, or shut down. The motivation for this exercise was developing sampling protocols for Australia’s Department of Agriculture and Water Resources for monitoring the biosecurity compliance of incoming goods at international borders. In this study, we show that maximum likelihood estimation of the failure rate under a sampling scheme can be biased depending on when estimation is performed, and we provide explicit expressions for the main contribution of the bias under various CSPs. We then construct bias-corrected estimators and confidence intervals, and evaluate their performance in a numerical study.

May 29, 2017 at 07:02PM

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from Geoffrey Decrouez, Andrew Robinson

http://ift.tt/2r4dWOc

Bias-Corrected Estimation in Continuous Sampling Plans

http://ift.tt/eA8V8J

Continuous sampling plans (CSPs) are algorithms used for monitoring and maintaining the quality of a production line. Although considerable work has been done on the development of CSPs, to our knowledge, there has been no corresponding effort in developing estimators with good statistical properties for data arising from a CSP inspection process. For example, information about the failure rate of the process will affect the management of the process, both in terms of selecting appropriate CSP parameters to keep the failure rate after inspection at a suitable level, and in terms of policy, for example, whether the process should be completely inspected, or shut down. The motivation for this exercise was developing sampling protocols for Australia’s Department of Agriculture and Water Resources for monitoring the biosecurity compliance of incoming goods at international borders. In this study, we show that maximum likelihood estimation of the failure rate under a sampling scheme can be biased depending on when estimation is performed, and we provide explicit expressions for the main contribution of the bias under various CSPs. We then construct bias-corrected estimators and confidence intervals, and evaluate their performance in a numerical study.

May 29, 2017 at 07:02PM

http://ift.tt/2r4dWOc

from Geoffrey Decrouez, Andrew Robinson

http://ift.tt/2r4dWOc

Bias-Corrected Estimation in Continuous Sampling Plans

http://ift.tt/eA8V8J

Continuous sampling plans (CSPs) are algorithms used for monitoring and maintaining the quality of a production line. Although considerable work has been done on the development of CSPs, to our knowledge, there has been no corresponding effort in developing estimators with good statistical properties for data arising from a CSP inspection process. For example, information about the failure rate of the process will affect the management of the process, both in terms of selecting appropriate CSP parameters to keep the failure rate after inspection at a suitable level, and in terms of policy, for example, whether the process should be completely inspected, or shut down. The motivation for this exercise was developing sampling protocols for Australia’s Department of Agriculture and Water Resources for monitoring the biosecurity compliance of incoming goods at international borders. In this study, we show that maximum likelihood estimation of the failure rate under a sampling scheme can be biased depending on when estimation is performed, and we provide explicit expressions for the main contribution of the bias under various CSPs. We then construct bias-corrected estimators and confidence intervals, and evaluate their performance in a numerical study.

May 29, 2017 at 07:02PM

http://ift.tt/2r4dWOc

from Geoffrey Decrouez, Andrew Robinson

http://ift.tt/2r4dWOc