AN ADAPTIVE TRIMMED LIKELIHOOD ALGORITHM FOR IDENTIFICATION OF MULTIVARIATE OUTLIERS

DOI10.1111/j.1467-842X.2006.00445.xzbMath1108.62030OpenAlexW2117783601MaRDI QIDQ3429841

Daniel Schubert, Brenton R. Clarke

Publication date: 20 March 2007

Published in: Australian <html_ent glyph="@amp;" ascii="&amp;"/> New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-842x.2006.00445.x

zbMATH Keywords

asymptotic varianceadaptive estimationMahalanobis distancetrimmed likelihood estimatorminimum covariance determinant estimatorforward search algorithm

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Robustness and adaptive procedures (parametric inference) (62F35) Monte Carlo methods (65C05)

Related Items (3)

A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering ⋮ Wild adaptive trimming for robust estimation and cluster analysis ⋮ The power of monitoring: how to make the most of a contaminated multivariate sample

Cites Work

• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Exploring multivariate data with the forward search.
• Robust diagnostic regression analysis
• A robust and efficient adaptive reweighted estimator of multivariate location and scatter.
• Least Median of Squares Regression
• Fast Very Robust Methods for the Detection of Multiple Outliers
• Identification of Outliers in Multivariate Data
• An adaptive method of estimation and outlier detection in regression applicable for small to moderate sample sizes
• The Masking Breakdown Point of Multivariate Outlier Identification Rules
• Asymptotics for an Adaptive Trimmed Likelihood Location Estimator
• Theory & Methods: A Note on Robustness of the β‐Trimmed Mean

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