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

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