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.
Original language | English |
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Pages (from-to) | 524-544 |
Number of pages | 21 |
Journal | Statistical Modelling |
Volume | 19 |
Issue number | 5 |
DOIs | |
State | Published - Oct 1 2019 |
Scopus Subject Areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Keywords
- Huber loss
- multivariate calibration
- P-splines
- robust regression
- signal regression