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Permanent link (DOI): https://doi.org/10.7939/R34H89

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Classification in the missing data Open Access

Descriptions

Other title
Subject/Keyword
Missing observations (Statistics)
Research -- Methodology
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Zhang, Xin
Supervisor and department
Dr. Ivan Mizera (Statistics)
Examining committee member and department
Dr. Sandra Garvie-Lok (Anthropology)
Dr. Peng Zhang (Statistics)
Department
Department of Mathematical and Statistical Sciences
Specialization

Date accepted
2010-08-20T16:38:10Z
Graduation date
2010-11
Degree
Master of Science
Degree level
Master's
Abstract
Missing data is always a problem when it comes to data analysis. This is especially the case in anthropology when sex determination is one of the primary goals for fossil skull data since many measurements were not available. We expect to find a classifier that can handle the large amount of missingness and improve the ability of prediction/classification as well. These are the objectives of this thesis. Besides of the crude methods (ignore cases with missingness), three possible techniques in handling of missing values are discussed: bootstrap imputation, weighted-averaging classifier and classification trees. All these methods do make use of all the cases in data and can handle any cases with missingness. The diabetes data and fossil skull data are used to compare the performance of different methods regarding to misclassification error rate. Each method has its own advantages and certain situations under which better performance will be achieved.
Language
English
DOI
doi:10.7939/R34H89
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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