Please use this identifier to cite or link to this item:
Title: Optimizing the quality of bootstrap-based prediction intervals
Authors: Khosravi, A.
Nahavandi, S.
Creighton, D.
Srinivasan, D. 
Issue Date: 2011
Source: Khosravi, A.,Nahavandi, S.,Creighton, D.,Srinivasan, D. (2011). Optimizing the quality of bootstrap-based prediction intervals. Proceedings of the International Joint Conference on Neural Networks : 3072-3078. ScholarBank@NUS Repository.
Abstract: 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. © 2011 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
ISBN: 9781457710865
DOI: 10.1109/IJCNN.2011.6033627
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Dec 11, 2017

Page view(s)

checked on Dec 9, 2017

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.