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Statistical Models Commons

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Full-Text Articles in Statistical Models

Gis-Integrated Mathematical Modeling Of Social Phenomena At Macro- And Micro- Levels—A Multivariate Geographically-Weighted Regression Model For Identifying Locations Vulnerable To Hosting Terrorist Safe-Houses: France As Case Study, Elyktra Eisman Nov 2015

Gis-Integrated Mathematical Modeling Of Social Phenomena At Macro- And Micro- Levels—A Multivariate Geographically-Weighted Regression Model For Identifying Locations Vulnerable To Hosting Terrorist Safe-Houses: France As Case Study, Elyktra Eisman

FIU Electronic Theses and Dissertations

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to …


Comparisons Of Estimators Of Small Proportion Under Group Testing, Xing Wei Jul 2015

Comparisons Of Estimators Of Small Proportion Under Group Testing, Xing Wei

FIU Electronic Theses and Dissertations

Binomial group testing has been long recognized as an efficient method of estimating proportion of subjects with a specific characteristic. The method is superior to the classic maximum likelihood estimator (MLE), particularly when the proportion is small. Under the group testing model, we assume the testing is conducted without error. In the present research, a new Bayes estimator will be proposed that utilizes an additional piece of information, the proportion to be estimated is small and within a given range. It is observed that with the appropriate choice of the hyper-parameter our new Bayes estimator has smaller mean squared error …