empirical processes based on the standardized residuals and their squares. For calculating P-values, the parametric bootstrap …

consider bootstrap-based inference. Interval estimation is addressed as well. Several bootstrap confidence intervals are …

“separability” and its mean, for which we assume the availability of only one dataset, using the leave-pair-out bootstrap …

P-values which do not suffer these defects, such as parametric bootstrap P-values or the partially maximised P-values of …

bootstrap tests use an exact calculation of the P-value, assuming nuisance parameters equal their null maximum likelihood …

-corrected likelihood ratio and score tests, and bootstrap-corrected tests. Our simulation results suggest that the corrected test we …

probability metric, a bootstrap approach and a stepwise search algorithm. Moreover, the procedure also allows one to identify …

We apply the generalized cluster bootstrap to both Gaussian quasi-likelihood and robust estimates in the context of … highly unbalanced clustered data. We compare it with the transformation bootstrap where the data are generated by the random … bootstrap performs better than the transformation bootstrap for highly unbalanced clustered data. We apply the generalized …

formulas for asymptotic variances requires the use of resampling methods. Unfortunately, the bootstrap (in any of its versions … theory and in practice) an appreciable number of the bootstrap replicates can no longer estimate a quantile that the original …-d” jackknife with d of order larger than n. Another alternative is to use randomly reweighted “bootstrap” samples with weights of …