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Multivariate statistical monitoring schemes are useful tools for quality improvement in industries like manufacturing, healthcare, and transportation among others. Practitioners use monitoring methods to detect significant changes in metrics of interest. This dissertation focuses on the problem of spatial-temporal surveillance of count data collected sequentially over time from a geographical area, such as the number of disease cases in a region or the number of crashes on a roadway with a purpose of detecting changes in rates of events. The research makes contributions to address in several challenges that are known to adversely affect the change detection performance of traditional statistical monitoring techniques. Among the challenges addressed are temporal autocorrelation and seasonality, non-homogeneity due to varying sample size, and parameter estimation errors in count data monitoring. We develop several new cumulative sum monitoring methodologies for count data to address these particular challenges. Comparative simulation analyses demonstrate advantages of the proposed approaches relative to some existing methods when accounting for temporal autocorrelation and seasonality, non-homogeneity from varying sample sizes, and parameter estimation errors in monitoring specific models of count data. Traffic crash data from the Florida Department of Transportation is used to illustrate the performance of the proposed methods for detecting shifts in crash count data.
A Dissertation submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Includes bibliographical references.
Omer Arda Vanli, Professor Directing Dissertation; Eric Chicken, University Representative; Samuel Awoniyi, Committee Member; Yanshuo Sun, Committee Member; Eren Ozguven, Committee Member; Joseph J. Pignatiello, Jr., Committee Member.
Florida State University
Giroux, R. R. (no date). Spatiotemporal Monitoring of Count Data: Applications of Roadway Safety. Retrieved from https://purl.lib.fsu.edu/diginole/2020_Spring_Giroux_fsu_0071E_15808