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Minimax Design for Approximate Straight Line Regression

  • Author / Creator
    Daemi, Maryam
  • This dissertation first reviews the construction of an optimal design for a straight linear regression model with uncorrelated errors when the experimenter seeks protection against the biases which will accrue if her straight line model is slightly erroneous. The optimal design is derived from the minimax method, and is robust against bias caused by a small departure from the fitted model. The study then points out a gap within the part of the minimax method related to minimizing the maximized loss function based on A- and E-optimality criteria: it is not applicable to finding an optimal design for these criteria when the emphasis is much more on the errors from bias than on those from variation. Finally, an alternative technique is applied in order to achieve an A- and E-optimal design whether the experimenter places more emphasis on the errors from bias or on the errors from variance.

  • Subjects / Keywords
  • Graduation date
    2012-09
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3BT54
  • 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
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Statistics
  • Supervisor / co-supervisor and their department(s)
    • Wiens, Douglas (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Prasad, N.G.Narasimha (Mathematical and Statistical Sciences)
    • Kong, Linglong (Mathematical and Statistical Sciences)
    • Szepesvari, Csaba (Computing Science)