Multivariate calibration with robust signal regression
Document Type
Article
Publication Date
10-1-2019
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.
Publication Source (Journal or Book title)
Statistical Modelling
First Page
524
Last Page
544
Recommended Citation
Li, B., Marx, B., Chakraborty, S., & Weindorf, D. (2019). Multivariate calibration with robust signal regression. Statistical Modelling, 19 (5), 524-544. https://doi.org/10.1177/1471082X18782813