Bootstrap resampling as a tool for calculating uncertainty measurement

Bootstrap resampling as a tool for radio - interferometric imaging fidelity assessment

We report on a numerical evaluation of the statistical bootstrap as a technique for radio-interferometric imaging fidelity assessment. The development of a fidelity assessment technique is an important scientific prerequisite for automated pipeline reduction of data from modern radio interferometers. We evaluate the statistical performance of two bootstrap methods, the model-based bootstrap and...

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the ...

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

Evaluating methods of calculating measurement uncertainty

This communication demonstrates the need for independent validation when an uncertainty calculation procedure is applied to a particular type of measurement problem. A simple measurement scenario is used to highlight differences in the performance of two general methods of uncertainty calculation, one from the Guide to the Expression of Uncertainty in Measurement (GUM) and one from Supplement 1...