Robust bivariate boxplots and multiple outlier detection.

DOI10.1016/S0167-9473(98)00040-1zbMath1042.62545OpenAlexW2014369660WikidataQ126377622 ScholiaQ126377622MaRDI QIDQ1274818

Aldo Corbellini, Marco Riani, Sérgio Luís Zani

Publication date: 12 January 1999

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(98)00040-1

zbMATH Keywords

Multiple outliersB-splineConvex hullBivariate boxplotRobust estimate

Mathematics Subject Classification ID

Nonparametric robustness (62G35)

Related Items (16)

The forward search and data visualization ⋮ Modelling neuromuscular blockade: a stochastic approach based on clinical data ⋮ A new multiple outliers identification method in linear regression ⋮ Forward search outlier detection in data envelopment analysis ⋮ Smaller representation of compiled regular expressions ⋮ Detection and correction of outliers in the bivariate chain-ladder method ⋮ Strong consistency and robustness of the forward search estimator of multivariate location and scatter ⋮ Robust estimation of efficient mean-variance frontiers ⋮ Clockwise Bivariate Boxplots ⋮ The Forward Search ⋮ Hubert, Rousseeuw and Segaert: ``Multivariate functional outlier detection ⋮ Inferences based on multiple skipped correlations ⋮ A fast algorithm to sample the number of vertexes and the area of the random convex hull on the unit square ⋮ Comparing robust generalized variances and comments on efficiency ⋮ The forward search: theory and data analysis ⋮ Exploratory tools for clustering multivariate data

Uses Software

• AS 307

Cites Work

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• Breakdown properties of location estimates based on halfspace depth and projected outlyingness
• An iterative method for the detection of multivariate outliers
• Computing depth contours of bivariate point clouds
• Least Median of Squares Regression
• Frequency Polygons: Theory and Application
• Performance of Some Resistant Rules for Outlier Labeling
• Fast Very Robust Methods for the Detection of Multiple Outliers

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