Robust Sampling Designs for Model-Based Stratification

  • Author / Creator
    Zhai, Zhichun
  • We study robust sampling designs for model-based stratification, when the assumed distribution F0 (·) of an auxiliary variable x, and the variance function g0 (·) in the associated regression model, are only approximately specified. We first maximize the scaled prediction mean squared error (SPMSE) for the empirical best predictor over the neighbourhoods of F0 and g0. Then we obtain robust sampling designs which minimize this maximum SPMSE through a modified genetic algorithm with ‘artificial implantation’. The techniques are illustrated in two case studies of Australian sugar farms and MU281 population.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Statistics
  • Supervisor / co-supervisor and their department(s)
    • Douglas P. Wiens (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Heo, Giseon (Mathematical and Statistical Sciences)
    • Mizera, Ivan (Mathematical and Statistical Sciences)
    • Prasad, NGN (Mathematical and Statistical Sciences)