Title page for ETD etd-03292011-155916


Type of Document Dissertation
Author Ryan, Anne Garrett
Author's Email Address agryan@vt.edu
URN etd-03292011-155916
Title Surveillance of Poisson and Multinomial Processes
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Woodall, William H. Committee Chair
Birch, Jeffrey B. Committee Member
Kim, Dong-Yun Committee Member
Reynolds, Marion R. Jr. Committee Member
Keywords
  • Average Run Length
  • Cumulative Sum Chart
  • Exponentially Weighted Moving Average Chart
  • Statistical Process Control
Date of Defense 2011-03-18
Availability unrestricted
Abstract
As time passes, change occurs. With this change comes the need for surveillance. One may be a technician on an assembly line and in need of a surveillance technique to monitor the number of defective components produced. On the other hand, one may be an administrator of a hospital in need of surveillance measures to monitor the number of patient falls in the hospital or to monitor surgical outcomes to detect changes in surgical failure rates. A natural choice for on-going surveillance is the control chart; however, the chart must be constructed in a way that accommodates the situation at hand. Two scenarios involving attribute control charting are investigated here. The first scenario involves Poisson count data where the area of opportunity changes. A modified exponentially weighted moving average (EWMA) chart is proposed to accommodate the varying sample sizes. The performance of this method is compared with the performance for several competing control chart techniques and recommendations are made regarding the best preforming control chart method. This research is a result of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech). The second scenario involves monitoring a process where items are classified into more than two categories and the results for these classifications are readily available. A multinomial cumulative sum (CUSUM) chart is proposed to monitor these types of situations. The multinomial CUSUM chart is evaluated through comparisons of performance with competing control chart methods. This research is a result of joint work with Mr. Lee J. Wells (Grado Department of Industrial and Systems Engineering, Virginia Tech) and Dr. William H. Woodall (Department of Statistics, Virginia Tech).
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