Files and links (1)
A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering28.05 MB
Published (Version of Record)CC BY V4.0, Open Access
Journal article
A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering
Statistical Papers, Vol.64, pp.395-420
2023
Abstract
This paper makes comparisons of automated procedures for robust multivariate outlier detection through discussion and simulation. In particular, automated procedures that use the forward search along with Mahalanobis distances to identify and classify multivariate outliers subject to predefined criteria are examined. Procedures utilizing a parametric model criterion based on a χ2-distribution are among these, whereas the multivariate Adaptive Trimmed Likelihood Algorithm (ATLA) identifies outliers based on an objective function that is derived from the asymptotics of the location estimator assuming a multivariate normal distribution. Several criterion including size (false positive rate), sensitivity, and relative efficiency are canvassed. To illustrate relative efficiency in a multivariate setting in a new way, measures of variability of the multivariate location parameter when the underlying distribution is chosen from a multivariate generalization of the Tukey–Huber ϵ-contamination model are used. Mean slippage models are also entertained. The simulation results here are illuminating and demonstrate there is no broadly accepted procedure that outperforms in all situations, albeit one may ascertain circumstances for which a particular method may be best if implemented. Finally the paper explores graphical monitoring for existence of clusters and the potential of classification through occurrence of multiple minima in the objective function using ATLA.
Details
- Title
- A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering
- Authors/Creators
- B.R. Clarke (Author/Creator) - Murdoch UniversityA. Grose (Author/Creator)
- Publication Details
- Statistical Papers, Vol.64, pp.395-420
- Publisher
- Springer
- Identifiers
- 991005544225907891
- Copyright
- © 2022 The Authors.
- Murdoch Affiliation
- School of Mathematics, Statistics, Chemistry and Physics
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
28 File views/ downloads
156 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 9 Mathematics
- 9.92 Statistical Methods
- 9.92.220 Robust Estimation
- Web Of Science research areas
- Statistics & Probability
- ESI research areas
- Mathematics