High finite-sample efficiency and robustness based on distance-constrained maximum likelihood

DOI10.1016/j.csda.2014.10.015OpenAlexW1998174596MaRDI QIDQ1623799

Víctor J. Yohai, Ricardo Antonio Maronna

Publication date: 23 November 2018

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

Full work available at URL: https://arxiv.org/abs/1311.5187

zbMATH Keywords

Kullback-Leibler divergencerobust regressionfinite-sample efficiencyrobust multivariate location and scatter

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items

Robust and sparse estimators for linear regression models, Discussion of ``The power of monitoring: how to make the most of a contaminated multivariate sample

Uses Software

• robustbase

Cites Work

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