Comparison of Three Methods of Generating Robust Statistical Designs

• Author / Creator

  Yu, Dengdeng

• This thesis deals with the problem proposed by Ye and Zhou (2007): Is a Q-optimal minimax design still symmetric if the requirement of $\int_\chi xm(x)dx = 0$ is removed? We have shown that for the simple linear regression, considering only the variance, a Q-optimal minimax design is necessarily symmetric; we have also made an attempt of addressing the symmetry problem considering only the bias which is much more difficult to achieve. However, the numerical results using three different algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Expected Improvement Algorithm (EIA), indicate that the claim is true. We have also applied the three algorithm on a non-linear cases correspondingly and make the comparison.

• Subjects / Keywords

  • Robust designs
  • Genetic algorithms
  • Expected Improvement
  • Particle Swarm Optimization
  • Symmetric Design

• Graduation date

  Fall 2013

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3JH3D872

• 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.