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Robust Variable Selection and Estimation in Threshold Regression Model

Robust Variable Selection and Estimation in Threshold Regression Model We combine the robust criterion with the lasso penalty together for the high-dimensional threshold model. It estimates regression coeffcients as well as the threshold parameter robustly that can be resistant to outliers or heavy-tailed noises and perform variable selection simultaneously. We illustrate our approach with the absolute loss, the Huber’s loss, and the Tukey’s loss, it can also be extended to any other robust losses. Simulation studies are conducted to demonstrate the usefulness of our robust approach. Finally, we use our estimators to investigate the presence of a shift in the effect of debt on future GDP growth. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

Robust Variable Selection and Estimation in Threshold Regression Model

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References (34)

Publisher
Springer Journals
Copyright
Copyright © The Editorial Office of AMAS & Springer-Verlag GmbH Germany 2020 2020
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/s10255-020-0939-y
Publisher site
See Article on Publisher Site

Abstract

We combine the robust criterion with the lasso penalty together for the high-dimensional threshold model. It estimates regression coeffcients as well as the threshold parameter robustly that can be resistant to outliers or heavy-tailed noises and perform variable selection simultaneously. We illustrate our approach with the absolute loss, the Huber’s loss, and the Tukey’s loss, it can also be extended to any other robust losses. Simulation studies are conducted to demonstrate the usefulness of our robust approach. Finally, we use our estimators to investigate the presence of a shift in the effect of debt on future GDP growth.

Journal

Acta Mathematicae Applicatae SinicaSpringer Journals

Published: Mar 11, 2020

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