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Activity Number:
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422
- Statistical Learning for Functional Data
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #328669
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Presentation
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Title:
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Multivariate Calibration with Robust Signal Regression
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Author(s):
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Bin Li* and Brian D. Marx and David C Weindorf and Somsubhra Chakraborty
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Companies:
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Louisiana State University and Louisiana State University and Texas Tech University and Indian Institute of Technology Kharagpur
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Keywords:
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Huber loss;
Multivariate calibration;
P-splines;
Robust Regression;
Signal Regression
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Abstract:
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Motivated by a multivariate calibration problem for a soil characterization study, we proposed tractable and robust variants of penalized signal regression (PSR) using a class of nonconvex Huber-like criteria as the loss function. Standard methods may fail to produce a reliable estimator when there are heavy-tailed errors. We present a computationally efficient algorithm to solve this nonconvex problem. Simulation and empirical examples are extremely promising and show the proposed algorithm substantially improves the PSR performance under heavy-tailed errors.
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Authors who are presenting talks have a * after their name.
