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

  • Robust designs
  • Sampling survey
  • Statistics

• Graduation date

  Fall 2014

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3833N52K

• 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.