Bootstrapping blurred and noisy data

Chan, K. and Kay, J. (1992) Bootstrapping blurred and noisy data. In: Dodge, Y. and Whittaker, J. (eds.) Computational Statistics Volume II: Proceedings of the 10th Symposium on Computational Statistics, COMPSTAT, Neuchâtel, Switzerland, August 1992. Physica-Verlag: Heidelberg, pp. 287-291. ISBN 9783642486807 (doi:10.1007/978-3-642-48678-4_35)

Full text not currently available from Enlighten.

Abstract

We consider the use of the bootstrap within the context of the restoration of an unknown signal from a version which has been corrupted by blur and noise. We briefly discuss three issues, namely using the bootstrap to select the smoothing parameter, to perform an adaptive restoration and to construct an interval estimate of the unknown signal at one or several points. We discuss some empirical results.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Kay, Dr Jim
Authors: Chan, K., and Kay, J.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Publisher:Physica-Verlag
ISBN:9783642486807

University Staff: Request a correction | Enlighten Editors: Update this record