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Permanent link (DOI): https://doi.org/10.7939/R3833N52K

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Robust Sampling Designs for Model-Based Stratification Open Access

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Other title
Subject/Keyword
Statistics
Sampling survey
Robust design
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Zhai, Zhichun
Supervisor and department
Douglas P. Wiens (Mathematical and Statistical Sciences)
Examining committee member and department
Mizera, Ivan (Mathematical and Statistical Sciences)
Heo, Giseon (Mathematical and Statistical Sciences)
Prasad, NGN (Mathematical and Statistical Sciences)
Department
Department of Mathematical and Statistical Sciences
Specialization
Statistics
Date accepted
2014-06-13T14:50:25Z
Graduation date
2014-11
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3833N52K
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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