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Permanent link (DOI): https://doi.org/10.7939/R3BT54

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Minimax Design for Approximate Straight Line Regression Open Access

Descriptions

Other title
Subject/Keyword
Robust Optimal Design
A-optimality
E-optimality
Minimax Design
Straight Line Regression
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Daemi, Maryam
Supervisor and department
Wiens, Douglas (Mathematical and Statistical Sciences)
Examining committee member and department
Prasad, N.G.Narasimha (Mathematical and Statistical Sciences)
Szepesvari, Csaba (Computing Science)
Kong, Linglong (Mathematical and Statistical Sciences)
Department
Department of Mathematical and Statistical Sciences
Specialization
Statistics
Date accepted
2012-09-28T15:01:54Z
Graduation date
2012-09
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3BT54
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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