Resampling a coverage pattern
AbstractThe possibility of resampling (bootstrapping) a spatial pattern is investigated. It is suggested that resampling provides a unified approach to consistemt inference in a wide range of coverage problems. Nevertheless, resampling distorts some of the interactions in the problem, and so introduces biases. The sizes of bias and standard deviation are investigated in the case of estimating sampling variance via resampling.
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Bibliographic InfoArticle provided by Elsevier in its journal Stochastic Processes and their Applications.
Volume (Year): 20 (1985)
Issue (Month): 2 (September)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description
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- Lahiri, Soumendra Nath, 1997. "On Inconsistency of the Jackknife-after-Bootstrap Bias Estimator for Dependent Data," Journal of Multivariate Analysis, Elsevier, vol. 63(1), pages 15-34, October.
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- Iranpanah, N. & Mohammadzadeh, M. & Taylor, C.C., 2011. "A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 578-587, January.
- Whitcher, Brandon, 2006. "Wavelet-based bootstrapping of spatial patterns on a finite lattice," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2399-2421, May.
- Wolfgang Hardle & Torsten Kleinow & Alexander Korostelev & Camille Logeay & Eckhard Platen, 2001. "Semiparametric Diffusion Estimation and Application to a Stock Market Model," Research Paper Series 51, Quantitative Finance Research Centre, University of Technology, Sydney.
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