What is it about?
A comparison of power and efficiency of multivariate outlier detection methods that use the forward search, plus an application to clustering of the Adaptive Trimmed Likelihood Algorithm (ATLA) for Multivariate Data.
Featured Image
Why is it important?
Until now ATLA had only been countenanced in the paper of Clarke and Schubert (2006) where it was introduced and had been overlooked in several comparisons. Here ATLA is shown to compete quite well with other methods that use the forward search, and in some scenarios does even better. In addition the approach leads to a natural clustering algorithm using the objective function.
Perspectives
This paper would have been written a lot earlier if it wasn't for the passing of Daniel Schubert in a motor bike accident in 2007. We are forever grateful for the code which we tweaked to make the comparisons.
Dr Brenton R. Clarke
Murdoch University
Read the Original
This page is a summary of: A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering, Statistical Papers, June 2022, Springer Science + Business Media,
DOI: 10.1007/s00362-022-01319-7.
You can read the full text:
Contributors
The following have contributed to this page
