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Optimizing the quality of bootstrap-based prediction intervals

conference contribution
posted on 2011-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton, D Srinivasan
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.

History

Event

International Joint Conference on Neural Network (2011 : San Jose, Calif.)

Pagination

3072 - 3078

Publisher

IEEE

Location

San Jose, Calif.

Place of publication

Piscataway, N. J.

Start date

2011-07-31

End date

2011-08-05

ISSN

2161-4393

ISBN-13

9781424496358

ISBN-10

1424496357

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2011, IEEE

Title of proceedings

IJCNN 2011 : Proceedings of the International Joint Conference on Neural Networks