robustHD

robustHD: Robust Methods for High-Dimensional Data. Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression.

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References in zbMATH (referenced in 8 articles , 1 standard article )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Bottmer, Lea; Croux, Christophe; Wilms, Ines: Sparse regression for large data sets with outliers (2022)
  2. Amato, Umberto; Antoniadis, Anestis; De Feis, Italia; Gijbels, Irene: Penalised robust estimators for sparse and high-dimensional linear models (2021)
  3. Andreas Alfons: robustHD: An R package for robust regression with high-dimensional data (2021) not zbMATH
  4. Filzmoser, Peter; Gregorich, Mariella: Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers (2020)
  5. Debruyne, Michiel; Höppner, Sebastiaan; Serneels, Sven; Verdonck, Tim: Outlyingness: which variables contribute most? (2019)
  6. Freue, Gabriela V. Cohen; Kepplinger, David; Salibián-Barrera, Matías; Smucler, Ezequiel: Robust elastic net estimators for variable selection and identification of proteomic biomarkers (2019)
  7. Alfons, Andreas; Croux, Christophe; Gelper, Sarah: Robust groupwise least angle regression (2016)
  8. Alfons, Andreas; Croux, Christophe; Gelper, Sarah: Sparse least trimmed squares regression for analyzing high-dimensional large data sets (2013)