Balanced Importance Resampling for the Bootstrap

Balanced bootstrap resampling method for neural model selection

Uniform resampling is the easiest to apply and is a general recipe for all problems, but it may require a large replication size B. To save computational effort in uniform resampling, balanced bootstrap resampling is proposed to change the bootstrap resampling plan. This resampling plan is effective for approximating the center of the bootstrap distribution. Therefore, this paper applies it to ...

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

Importance Resampling for BSSRDF

Rendering translucent effects using the diffusion model BSSRDF [Jensen et al. 2001] is still a difficult problem compared to traditional BSDF, because it introduces additional degrees of freedom. In a BSSRDF, the light does not scatter at a single point: it enters the surface at one point, and exits at another. Various techniques have been proposed to sample an exit point based on a entry point...

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...