Multivariate calibration with robust signal regression. (October 2019)

• Record Type: Journal Article
• Authors: Li, Bin; Marx, Brian D; Chakraborty, Somsubhra; Weindorf, David C
• Is Part Of: Statistical modelling. Volume 19:Number 5(2019)
• Page Start: 524
• Page End: 544
• Publication Date: 2019-10
• Subjects: Huber loss -- multivariate calibration -- P-splines -- robust regression -- signal regression; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X18782813
• Languages: English
• ISSNs: 1471-082X

Abstract

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.