University of Alberta Robust Sampling Designs for Model-Based Stratification

Permission is hereby granted to the University of Alberta Library 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 whatever without the author’s prior written permission. 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. Acknowledgements I would like to thank my supervisor, Professor Douglas P. Wiens, for his guidance, supervision and encouragement during my master program, which was supported b