Statistical Modelling 19 (5) (2019), 524–544
Multivariate calibration with robust signal regression
Bin Li,
Department of Experimental Statistics,
Louisiana State University,
Baton Rouge, LA,
USA.
e-mail: bli@lsu.edu
Brian D Marx,
Department of Experimental Statistics,
Louisiana State University,
Baton Rouge, LA,
USA.
Somsubhra Chakraborty,
Agricultural and Food Engineering Department,
IIT Kharagpur,
Kharagpur, West Bengal,
India.
David C Weindorf,
Department of Plant and Soil Science,
Texas Tech University,
Lubbock, TX,
USA.
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.
Keywords:
Huber loss; multivariate calibration; P-splines; robust regression; signal regression.
Downloads:
Example data and code in
zipped archive.
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