[图书][B] Robust statistics: theory and methods (with R)

RA Maronna, RD Martin, VJ Yohai, M Salibián-Barrera - 2019 - books.google.com
A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …

Robust and sparse estimators for linear regression models

E Smucler, VJ Yohai - Computational Statistics & Data Analysis, 2017 - Elsevier
Penalized regression estimators are popular tools for the analysis of sparse and high-
dimensional models. However, penalized regression estimators defined using an …

[PDF][PDF] A review of robust regression in biomedical science research

S Varin, DB Panagiotakos - Archives of Medical Science, 2020 - termedia.pl
It is a fact that most real-world datasets in biomedical research contain outliers and leverage
points. To define what an outlier and a leverage point is, let us assume a Y\X regression …

Robust variable selection under cellwise contamination

P Su, G Tarr, S Muller - Journal of Statistical Computation and …, 2024 - Taylor & Francis
Cellwise outliers are widespread in real world data analysis. Traditional robust methods may
fail when applied to datasets under such contamination. We introduce a variable selection …

Robust estimation and variable selection for cellwise contaminated data

P Su - 2023 - ses.library.usyd.edu.au
Outliers are widespread in real-world datasets. Recognizing outliers and running robust
analyses is still a challenging topic. Recently, there has been increased attention on …

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T Statistics - Wiley Online Library
All submissions to the journal should be submitted online at http://mc. manuscriptcentral.
com/teachstats. Full instructions and support are available on the site and a user ID and …