Bio & Expertise
Dr. Raid Amin is a Distinguished University Professor in the Mathematics and Statistics Department. Amin, who joined UWF in 1987, is one of the first faculty members to win the UWF Faculty Catalyst Initiative Award. He has numerous research interests. They include statistical process control and geospatial cluster analysis on a variety of variables, such as crimes, cancer rates, sexual offenders and predators. Amin’s other research has ranged from the study of human-shark interaction to student learning styles to statistical consulting for clinical trials in medical research.
Amin’s research has been published in books and dozens of journals. Some of the most prominent articles appeared in Technometrics, Journal of Quality Technology, Journal of Environment and Ecology, Animal Cognition, Statistics and Public Policy, Journal of Coastal Research, and Sequential Analysis. Amin also is a statistical consultant for medical training programs at two Pensacola healthcare organizations. At Sacred Heart Hospital he works with resident physicians in their residency projects. At West Florida Hospital he works with resident pharmacists in their residency projects.
He has three degrees in Statistics: a M.S. and a Ph. D. from Virginia Polytechnic Institute and State University and a B.S. from Baghdad University, Iraq. He has been a reviewer for Technometrics, Journal of Quality Technology, Journal of Statistical Computation and Simulation, International Statistical Review and other scholarly publications. Most recently, the Environmental Protection Agency chose UWF to officially be a partner in EPA’s Toxic Release Inventory University Challenge Program after it selected as a winner Amin’s proposal to teach a research course with UWF students to analyze EPA data sets.
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Highlights - Scholarship
Journal article
Geographical Clusters of Rape in the United States: 2000-2012
Published 01/01/2015
Statistics and Public Policy, 2, 1, 87 - 92
While rape is a very serious crime and public health problem, no spatial mapping has been attempted for rape on the national scale. This article addresses the three research questions: (1) Are reported rape cases randomly distributed across the United States, after being adjusted for population density and age, or are there geographical clusters of reported rape cases? (2) Are the geographical clusters of reported rapes still present after adjusting for differences in poverty levels? (3) Are there geographical clusters where the proportion of reported rape cases that lead to an arrest is exceptionally low or exceptionally high? We studied the geographical variation of reported rape events (2003-2012) and rape arrests (2000-2012) in the 48 contiguous states of the USA. The disease surveillance software SaTScan (TM) with its spatial scan statistic is used to evaluate the spatial variation in rapes. The spatial scan statistic has been widely used as a geographical surveillance tool for diseases, and we used it to identify geographical areas with clusters of reported rape and clusters of arrest rates for rape. The spatial scan statistic was used to identify geographical areas with exceptionally high rates of reported rape. The analyses were adjusted for age, and in secondary analyses, for both age and poverty level. We also identified geographical areas with either a low or a high proportion of reported rapes leading to an arrest. We have identified geographical areas with exceptionally high (low) rates of reported rape. The geographical problem areas identified are prime candidates for more intensive preventive counseling and criminal prosecution efforts by public health, social service, and law enforcement agencies. Geographical clusters of high rates of reported rape are prime areas in need of expanded implementation of preventive measures, such as changing attitudes in our society toward rape crimes, in addition to having the criminal justice system play an even larger role in preventing rape.
Journal article
A nonparametric exponentially weighted moving average control scheme
Published 01/01/1991
Communications in statistics. Simulation and computation, 20, 4, 1049 - 1072
A common assumption when evaluating the properties of Exponentially Weighted Moving Average (EWMA) control procedures for controlling the process mean is the observations are normal with known variance. In this article we propose a nonparametric control procedure that can be used when the underlying distribution is not know there is not enough information on the variance or shape of the distribution. Its average run length properties are less affected than the corresponding parametric EWMA procedure when autocorrelation between the observations is present. The procedure nonparametric EWMA procedure is based on Wilcoxon signed-rank statistics ranking is within groups. Our simulation results show that the proposed control procedure is less efficient than the parametric
-EWMA procedure when the distribution is not and it can be considerably more efficient than the parametric procedure for heavy- distributions. The proposed procedure is insensitive to misspecification of the van and its ARL properties of the control procedure are affected relatively little by cho the weighting parameter λ.