Multivariate calibration with robust signal regression

Bin Li, Brian D. Marx, Somsubhra Chakraborty, David C. Weindorf

Research output: Contribution to journalArticlepeer-review


Motivated by a multivariate calibration problem from a soil characterization study, we proposed tractable and robust variants of penalized signal regression (PSR) using a class of non-convex Huber-like criteria as the loss function. Standard methods may fail to produce a reliable estimator, especially when there are heavy-tailed errors. We present a computationally efficient algorithm to solve this non-convex problem. Simulation and empirical examples are extremely promising and show that the proposed algorithm substantially improves the PSR performance under heavy-tailed errors.

Original languageEnglish
Pages (from-to)524-544
Number of pages21
JournalStatistical Modelling
Issue number5
StatePublished - Oct 1 2019


  • Huber loss
  • P-splines
  • multivariate calibration
  • robust regression
  • signal regression


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