approximations as well as an estimating function bootstrap (EFB) method based on resampling the estimated terms in the estimating …

asymptotic distributions can be consistently estimated by the nonparametric bootstrap. In Chapter 3 we investigate the accuracy … of inference based on the asymptotic normal and bootstrap approximations, and provide bounds on the associated error. In …

The paper develops the bootstrap theory and extends the asymptotic theory of rank estimators, such as the Maximum Rank … asymptotic distributions can be consistently estimated by the nonparametric bootstrap. We investigate the accuracy of inference … based on the asymptotic approximation and the bootstrap, and provide bounds on the associated error. In the case of MRC and …

The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically …, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it … requires repeated re-calculation of the estimator. In Honoré and Hu (2015), we propose a computationally simpler bootstrap …

, propose a bootstrap version of the test, and conduct a small Monte Carlo simulation to evaluate the finitesample performance …

The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically …, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it … requires repeated re-calculation of the estimator. In Honoré and Hu (2015), we propose a computationally simpler bootstrap …