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 …

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 …

we refer to as the analytic and bootstrap-after-cross-validation methods. For both methods, we derive nonasymptotic error …

-estimator, which we refer to as the analytic and bootstrap-aftercross-validation methods. For both methods, we derive nonasymptotic …

methods for selecting bandwidths that under- or oversmooth. This paper extends the bootstrap method of Hall and Horowitz (2013 …) for conditional mean functions to conditional quantile functions. The paper also shows how the bootstrap method can be … used to obtain uniform confidence bands. The bootstrap method uses only bandwidths that are selected by standard methods …

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of …" bootstrap. We investigate the first order asymptotic properties of the kernel block bootstrap method for quasi …-maximum likelihood demonstrating, in particular, its consistency and the first-order asymptotic validity of the bootstrap approximation …

estimation, especially, when researchers use bootstrap to estimate standard errors, which may be wrong without a global estimator …

distribution of a suitable estimator θ^n, its bootstrap approximation, and the Bayesian posterior for θ all agree asymptotically …(θ) and the bootstrap distribution of g(θ^n) coincide asymptotically. One implication is that Bayesians can interpret … bootstrap inference for g(θ) as approximately valid posterior inference in a large sample. Another implication−built on known …

This article generalizes and extends the kernel block bootstrap (KBB) method of Parente and Smith (2018, 2021) to … moment conditions. KBB procedures that employ bootstrap distributions with generalised empirical likelihood implied … bootstrap distributions in the extant literature. Simulation experiments reveal that critical values arising from the empirical …