• : This talk considers a series of manufactured parts, where each part is composed of the same specified regions. Part of the manufacturing process includes counting the number of occurrences of a characteristic in each region for each part. However, after collecting data in this manner for some time, a more expansive data collection method is utilized and the number of regions is increased, such that at least some of the new regional boundaries are different from the original boundaries. While the more numerous regions allow this new setting to describe the locations of occurrences with more precision, the number of observations that can be collected in the new setting is limited (e.g., due to time or financial restrictions). The ultimate goal is to estimate the average number of occurrences for the more numerous regions using both sets of data. This set-up is motivated by a proprietary application at Los Alamos National Lab; thus, simulated data is used in our study. Maximum Likelihood and Bayesian Estimators of the mean number of occurrences in the second setting are explored using analytical and simulation results from the combined datasets. Although this methodology will be discussed in terms of the manufacturing application, this technique and our code can be applied to other settings.
  • : Michaela Brydon, Erin Leatherman, Kenneth Ryan, Michael Hamada
  • : Kenyon College, Kenyon College, West Virginia University, Los Alamos National Lab
  • : Michaela Brydon
  • : statistics
  • : introductory/practitioner
  • : brydon1@kenyon.edu
  • : 7244213694
Poisson Count Estimation