Designs for nonlinear regression with a prior on the parameters

• Author / Creator

  Karami, Jamil

• This thesis deals with finding design points for nonlinear regression models with the possibility that the fitted model is incorrect. The information matrix depends on the parameter in nonlinear situations. We have assumed a range of values of the parameter and have specified a prior on the parameter space. A loss function has been developed and then a minimax approach has been adopted to achieve our goal. We have found an explicit expression for the maximized loss and a numerical minimization of it has been done by a genetic algorithm (GA). The whole approach has been implemented by considering some well-known nonlinear functions. We see that changing the values of the parameter of the prior density have effect on design points. However, changing the tuning constants of GA does not alter the design points noticeably. This indicates that we have obtained the minimizing design.

• Subjects / Keywords

  • Crossover
  • Minimax design
  • Mutations
  • Genetic algorithms
  • Designs
  • Robustness
  • GA
  • Loss function
  • Nonlinear regression
  • Information matrix

• Graduation date

  Fall 2011

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3W121

• 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.