Estimations use a block bootstrap.
For each extreme index, we draw 540 samples of 85-year time series (i.e., 20 samples from each of the 27 climate models) using a block-bootstrap approach, which is implemented as follows: We first draw a sample of 85 consecutive years with replacement from the long simulations and then simultaneously draw the simulations at all grid cells falling in this time period.
To test the role of internal variability in creating persistent changes, we implement our analysis on an ensemble of 540 85-year time series of temperature extremes, drawn from the bias-corrected 'piControl' simulations using a block-bootstrap approach (see Methods).
We test the significance of our results using a nonparametric block bootstrap method [ Wilks, 2006; Smith et al., 2013] (see supporting information for further details).
This paper presented and compared WNN and artificial neural network (ANN), both of which were combined with the ensemble method using block bootstrap sampling (BB), in terms of the forecast accuracy and precision at various lead-times on the Bow River, Alberta, Canada.
To study the significance of the co-occurrence of the different ChIP-Seq clusters in specific regions we used the block bootstrap and segmentation method developed in the Encode project [ 82].
This random simulation procedure gave essentially the same pattern of significance (supplementary table S2, Supplementary Material online) and thus for most analyses we only used the block bootstrap procedure.
Therefore, following Bertrand et al. (2004), we also estimated the models using block bootstrapped standard errors, using the same 64 clusters and resampling 10,000 times.
We validated each model using a bootstrap method (10,000-iteration) and reported the bootstrap bias-corrected AUCs with 95% bootstrap confidence interval (CI) (15).
