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A Multivariate Adaptive Trimmed Likelihood Algorithm

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.

Identiferoai:union.ndltd.org:ADTP/221763
Date January 2005
CreatorsDaniel.Schubert@csiro.au, Daniel Schubert
PublisherMurdoch University
Source SetsAustraliasian Digital Theses Program
LanguageEnglish
Detected LanguageEnglish
Rightshttp://www.murdoch.edu.au/goto/CopyrightNotice, Copyright Daniel Schubert

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