Robust clustering around regression lines with high density regions

DOI10.1007/s11634-013-0151-5zbMath1474.62217OpenAlexW2007443059MaRDI QIDQ2009033

Domenico Perrotta, Andrea Cerioli

Publication date: 27 November 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-013-0151-5

zbMATH Keywords

thinningtrimminginternational tradeoutlier detectionTCLUSTorthogonal regressionconcentrated noiseanti-fraudrobust clusterwise regression

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Trade models (91B60)

Related Items (13)

Modeling international trade data with the Tweedie distribution for anti-fraud and policy support ⋮ Assessing trimming methodologies for clustering linear regression data ⋮ Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library ⋮ Reliable Robust Regression Diagnostics ⋮ Combining homogeneous groups of preclassified observations with application to international trade ⋮ Outlier-free merging of homogeneous groups of pre-classified observations under contamination ⋮ On Characterizations and Tests of Benford’s Law ⋮ Covariance matrices of S robust regression estimators ⋮ Forum on Benford's law and statistical methods for the detection of frauds ⋮ RobROSE: a robust approach for dealing with imbalanced data in fraud detection ⋮ Semiautomatic robust regression clustering of international trade data ⋮ Tempered positive Linnik processes and their representations ⋮ Discussion of ``The power of monitoring: how to make the most of a contaminated multivariate sample

Uses Software

• spatstat
• TCLUST

Cites Work

• Robust fitting of mixtures using the trimmed likelihood estimator
• Robust fitting of mixture regression models
• Robust analysis of bibliometric data
• A new dimension reduction method: Factor discriminant \(K\)-means
• Linear grouping using orthogonal regression
• Exploring multivariate data with the forward search.
• A review of robust clustering methods
• A simulation study to compare robust clustering methods based on mixtures
• Robust clusterwise linear regression through trimming
• Simultaneous component and clustering models for three-way data: Within and between approaches
• The forward search: theory and data analysis
• Robust Linear Clustering
• A Kernel Method for Smoothing Point Process Data
• Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
• Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Statistical Analysis and Modelling of Spatial Point Patterns

This page was built for publication: Robust clustering around regression lines with high density regions