We use a subsample bootstrap method to get a consistent estimate of the asymptotically optimal choice of the …

We consider theoretical bootstrap "coupling" techniques for nonparametric robust smoothers and quantile regression, and … verify the bootstrap improvement. To cope with curse of dimensionality, a variant of "coupling" bootstrap techniques are …. Our bootstrap method can be used in many situations like constructing con dence intervals and bands. We demonstrate the …

functionals of kernel-type estimators (1 < p < ∞) and is easy to implement in general, mainly due to its recourse to the bootstrap … method. The bootstrap procedure is based on nonparametric bootstrap applied to kernel-based test statistics, with estimated … "contact sets". We provide regularity conditions under which the bootstrap test is asymptotically valid uniformly over a large …

discrepancy between the IV smiles for levered and unlevered ETF options. We construct bootstrap uniform confidence bands which …

semiparametric efficient estimate of the unknown distribution function of the data. One-step, two-step and bootstrap empirical … bootstrap methods improve upon this result. …

distribution depend on unknown nuisance parameters. A bootstrap method is used to obtain more reliable inference. The regularized …

theta_n, its bootstrap approximation, and the Bayesian posterior for all agree asymptotically. It is shown that whenever g … is Lipschitz, though not necessarily differentiable, the posterior distribution of g(theta) and the bootstrap … distribution of g(theta_n) coincide asymptotically. One implication is that Bayesians can interpret bootstrap inference for g …