Ensemble robust SIMPLS with block-penalized smoothing for scalar-on-function regression


ALIN A.

Chemometrics and Intelligent Laboratory Systems, cilt.269, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 269
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.chemolab.2025.105626
  • Dergi Adı: Chemometrics and Intelligent Laboratory Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Chimica, Compendex, EMBASE, INSPEC
  • Anahtar Kelimeler: Bootstrap, Functional data, Multicollinearity, Outliers, Penalization, PLSR, Robustness
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

We propose a robust penalized smooth partial least squares approach that (i) smooths high-dimensional discretized functional predictors via blockwise B-spline bases, (ii) applies robust SIMPLS to obtain latent scores, (iii) fits a penalized regression in the latent space whose penalty is exactly a block-diagonal roughness penalty on the coefficient function(s), and (iv) aggregates models through bootstrap ensembles (classical/sufficient resampling; mean/median aggregation). The method supports multiple functional predictors through a block-diagonal construction and yields interpretable smooth coefficient functions. Our method demonstrates competitive or superior performance under collinearity and contamination.