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.

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Title Optimizing the quality of bootstrap-based prediction intervals
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DougORCID iD for 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

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