Multiple outliers detection in sparse high-dimensional regression

DOI10.1080/00949655.2017.1379521OpenAlexW2754184174WikidataQ57834344 ScholiaQ57834344MaRDI QIDQ4960528

Bin Chen, Zhonghua Li, Tao Wang, Qun Li

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2017.1379521

zbMATH Keywords

multiple hypothesis testinghigh-dimensional linear regressionleast trimmed squaremultiple outliers detection

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15) Robustness and adaptive procedures (parametric inference) (62F35) Diagnostics, and linear inference and regression (62J20)

Related Items (7)

Robust sparse regression by modeling noise as a mixture of gaussians ⋮ Robust difference-based outlier detection ⋮ A new correction approach for information criteria to detect outliers in regression modeling ⋮ A new multiple outliers identification method in linear regression ⋮ Asymmetric influence measure for high dimensional regression ⋮ Detecting outliers and influential points: an indirect classical Mahalanobis distance-based method ⋮ A new outlier detection method based on convex optimization: application to diagnosis of Parkinson’s disease

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