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Murdoch University Digital Theses Program
A Multivariate Adaptive Trimmed Likelihood Algorithm
| Author Information |
Thesis Files |
| Last Name |
Schubert
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| Other Names |
Daniel
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| Title |
Doctor
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| E-mail |
Daniel.Schubert@csiro.au
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| Division |
Science & Engineering
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| School |
Maths & Statistics
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| Degree Program |
Doctor of Philosophy (PhD)
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| Thesis Document Information |
| Thesis Type |
PhD Doctorate
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| Title
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A Multivariate Adaptive Trimmed Likelihood Algorithm
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| Date |
2005
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| Abstract
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The research reported in this thesis describes a new algorithm which can be used to
robustify statistical estimates adaptively. The algorithm does not require any pre-specified
cut-off value between inlying and outlying regions and there is no presumption of any
cluster configuration. This new algorithm adapts to any particular sample and may advise
the trimming of a certain proportion of data considered extraneous or may divulge the
structure of a multi-modal data set. Its adaptive quality also allows for the confirmation
that uni-modal, multivariate normal data sets are outlier free. It is also shown to behave
independently of the type of outlier, for example, whether applied to a data set with a
solitary observation located in some extreme region or to a data set composed of clusters
of outlying data, this algorithm performs with a high probability of success.
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| Committee Information
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| Supervisor |
Dr. Brenton Clarke
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| Email |
B.Clarke@murdoch.edu.au
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