Optimizing the quality of bootstrap-based prediction intervals Khosravi, Abbas, Nahavandi, Saeid, Creighton, Doug and Srinivasan, Dipti 2011, Optimizing the quality of bootstrap-based prediction intervals, in IJCNN 2011 : Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, N. J., pp. 3072-3078. Attached Files Name Description MIMEType Size Downloads Title Optimizing the quality of bootstrap-based prediction intervals Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Creighton, Doug orcid.org/0000-0002-9217-1231 Srinivasan, Dipti Conference name International Joint Conference on Neural Network (2011 : San Jose, Calif.) Conference location San Jose, Calif. Conference dates 31 July-5 Aug. 2011 Title of proceedings IJCNN 2011 : Proceedings of the International Joint Conference on Neural Networks Editor(s) [Unknown] Publication date 2011 Conference series International Joint Conference on Neural Network Start page 3072 End page 3078 Total pages 6 Publisher IEEE Place of publication Piscataway, N. J. Keyword(s) bootstrap method coverage probabilities neural network parameters prediction interval simulated annealing method Summary The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals. ISBN 1424496357 9781424496358 ISSN 2161-4393 Language eng Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified Socio Economic Objective 919999 Economic Framework not elsewhere classified HERDC Research category E1 Full written paper - refereed Copyright notice ©2011, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30042231 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 14 Feb 2012, 15:21:11 EST Thu, 15 Mar 2012, 14:20:41 EST Thu, 15 Mar 2012, 14:22:09 EST Thu, 22 Mar 2012, 14:52:21 EST Fri, 23 Mar 2012, 08:44:00 EST Fri, 23 Mar 2012, 08:45:59 EST Wed, 23 May 2012, 14:31:08 EST Wed, 02 Jul 2014, 15:29:25 EST Wed, 16 Jul 2014, 11:06:15 EST Fri, 17 Oct 2014, 11:17:53 EST Wed, 21 Oct 2015, 17:33:46 EST Filtered Full Citation counts:   Cited 13 times in TR Web of Science Cited 17 times in Scopus Search Google Scholar Access Statistics: 765 Abstract Views, 9 File Downloads  -  Detailed Statistics Created: Tue, 14 Feb 2012, 15:21:11 EST

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