Empirical likelihood block bootstrapping

Regenerative block empirical likelihood for Markov chains

Bootstrapping general empirical measures

It is proved that the bootstrapped central limit theorem for empirical processes indexed by a class of functions F and based on a probability measure P holds a.s. if and only if F CLT (P ) and ∫ F dP < ∞, where F = supf F |f | and it holds in probability if and only if F ∈ CLT (P ). Thus, for a large class of statistics, no local uniformity of the CLT (about P ) is needed for the bootstrap to w...

Block-bootstrapping for noisy data.

BACKGROUND Statistical inference of signals is key to understand fundamental processes in the neurosciences. It is essential to distinguish true from random effects. To this end, statistical concepts of confidence intervals, significance levels and hypothesis tests are employed. Bootstrap-based approaches complement the analytical approaches, replacing the latter whenever these are not possible...

On block bootstrapping areal data

Inference for random processes depends on the estimation of the long-run variance of a statistic. Commonly used approaches in time series analysis for estimating the long-run variance include bootstrap approaches and the class of heteroskedasticity and autocorrelation consistent covariance estimators. These approaches have been generalized to deal with point processes and continuous spatial pro...

Empirical Likelihood

Empirical likelihood is a technique for forming hypothesis tests and conndence sets based on nonparametric likelihood ratios. Many properties of parametric likelihood ratio functions have nonparametric parallels. The main one is that there is a nonparametric version of Wilks's famous result wherein an asymptotic chisquare distribution holds for the log likelihood ratio. The nonparametric versio...