Assessing trimming methodologies for clustering linear regression data

DOI10.1007/s11634-018-0331-4zbMath1459.62010OpenAlexW2885572615WikidataQ129481345 ScholiaQ129481345MaRDI QIDQ2418092

Marco Riani, Francesca Torti, Andrea Cerioli, Domenico Perrotta

Publication date: 3 June 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/s11634-018-0331-4

zbMATH Keywords

Monte Carlo experimentmixture modelingclusterwise regressionrobust clusteringMixSimRegTCLUST-REGTCWRM

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items

Robust fitting of mixtures of GLMs by weighted likelihood ⋮ Cluster analysis with cellwise trimming and applications for the robust clustering of curves ⋮ Outlier-free merging of homogeneous groups of pre-classified observations under contamination ⋮ Weighted likelihood latent class linear regression ⋮ Semiautomatic robust regression clustering of international trade data ⋮ Tempered positive Linnik processes and their representations

Uses Software

• MixSim
• FSDA
• TCLUST
• AS 155

Cites Work

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