, as an alternative to the asymptotic approach, we propose a bootstrap confidence band. The developed theory is helpful for …

An iterated bootstrap confidence interval requires an additive correction to be made to the nominal coverage level of … involving two nested levels of bootstrap sampling. Asymptotic expansions of the required correction and the iterated interval … bootstrap confidence interval. The first asymptotic interval replaces the need for a second level of bootstrap sampling with a …

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross …-sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent … bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external …

dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard context and show that it …

bootstrap methods. Specifically, since standard wild bootstrap procedures deliver inconsistent inference, we propose a local … Gaussian (LG) bootstrap, establish its first-order asymptotic validity, and use Edgeworth expansions to show that the LG … bootstrap inference achieves second-order asymptotic refinements. Moreover, we provide new Laplace transform-based estimators of …

efficiency level of state-owned banks. The bootstrap approach demonstrates that the majority of estimates obtained in this study …

distribution is shown to be a scaled chi-quare distribution. Bootstrap and empirical likelihood based inferential methods for the …

under two ROC curves (AUCs) in the interested specificity interval. In this thesis, we have proposed bootstrap and empirical … simulation studies to compare four proposed EL and bootstrap based intervals. …