Stacking with dual bootstrap resampling

Stacking with dual bootstrap resampling

S U M M A R Y A new kind of stacking scheme, based on the hypothesis testing of signal significance and coherence, is proposed. The significance of stacked data is evaluated by running two kinds of bootstrap resampling, one for standard bootstrap and the other for preparing noise stacks by scrambling relative time-shifts between traces. This dual bootstrap procedure allows us to formulate a two...

Efficient Bootstrap Resampling

Bootstrap principle is briefly reviewed. Hall’s (1989) antithetic variates method for bootstrap is discussed and extended to more than two antithetic resampling processes. We illustrate the theory with a simulation study. The numerical results show that increasing the number of antithetic resampling processes produces significant smaller variances of the bootstrap estimator over the paired case.

- multiscale bootstrap resampling

Statistics Notes: Bootstrap resampling methods.

the bmj | BMJ 2015;350:h2622 | doi: 10.1136/bmj.h2622 1Department of Health Sciences, University of York, York YO10 5DD, UK 2Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; Correspondence to: J M Bland [email protected] Cite this as: BMJ 2015;350:h2622 doi: 10.1136/bmj.h2622 Statis...

Improving Classification Accuracy Assessments with Statistical Bootstrap Resampling Techniques

The use of remotely sensed imagery to generate land cover models is common today. Validation of these models typically involves the use of an independent set of ground-truth data which are used to calculate an error matrix resulting in estimates of omission, commission, and overall error. However, each estimate of error contains a degree of uncertainty itself due to 1) conceptual bias, 2) locat...