Robust and sparse estimation methods for high-dimensional linear and logistic regression

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KURNAZ F. S., Hoffmann I., Filzmoser P.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, cilt.172, ss.211-222, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 172
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.chemolab.2017.11.017
  • Dergi Adı: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.211-222
  • Anahtar Kelimeler: Elastic net penalty, Least trimmed squares, C-step algorithm, High-dimensional data, Robustness, Sparse estimation, LARGE DATA SETS, SQUARES REGRESSION, REGULARIZATION, SELECTION, MODELS
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Fully robust versions of the elastic net estimator are introduced for linear and logistic regression. The algorithms used to compute the estimators are based on the idea of repeatedly applying the non-robust classical estimators to data subsets only. It is shown how outlier-free subsets can be identified efficiently, and how appropriate tuning parameters for the elastic net penalties can be selected. A final reweighting step improves the efficiency of the estimators. Simulation studies compare with non-robust and other competing robust estimators and reveal the superiority of the newly proposed methods. This is also supported by a reasonable computation time and by good performance in real data examples.