Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Learning under signal-to-noise ratio uncertainty

Learning under signal-to-noise ratio uncertainty Abstract The paper presents an alternative real time adaptive learning algorithm in the presence of signal-to-noise ratio uncertainty. The main innovation of this algorithm is that it uses a gain which is determined within the model: it continuously depends on the extent of misevaluation of parameters embedded in the forecast error. We show that in the presence of signal-to-noise ratio misevaluation, the usage of the proposed learning algorithm is a significant improvement on the Kalman Filter learning algorithm. In a full information case, the Kalman Filter learning algorithm is still the optimal tool. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

Learning under signal-to-noise ratio uncertainty

Download PDF
37 pages

Loading next page...
 
/lp/de-gruyter/learning-under-signal-to-noise-ratio-uncertainty-j003sx0mN0

References (19)

Publisher
de Gruyter
Copyright
Copyright © 2013 by the
ISSN
1081-1826
eISSN
1558-3708
DOI
10.1515/snde-2012-0046
Publisher site
See Article on Publisher Site

Abstract

Abstract The paper presents an alternative real time adaptive learning algorithm in the presence of signal-to-noise ratio uncertainty. The main innovation of this algorithm is that it uses a gain which is determined within the model: it continuously depends on the extent of misevaluation of parameters embedded in the forecast error. We show that in the presence of signal-to-noise ratio misevaluation, the usage of the proposed learning algorithm is a significant improvement on the Kalman Filter learning algorithm. In a full information case, the Kalman Filter learning algorithm is still the optimal tool.

Journal

Studies in Nonlinear Dynamics & Econometrics – de Gruyter

Published: Feb 14, 2013

There are no references for this article.