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.
| Identifer | oai:union.ndltd.org:ADTP/221763 |
| Date | January 2005 |
| Creators | Daniel.Schubert@csiro.au, Daniel Schubert |
| Publisher | Murdoch University |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.murdoch.edu.au/goto/CopyrightNotice, Copyright Daniel Schubert |
Page generated in 0.0014 seconds