The bootstrap is a versatile inference method that has proven powerful in many statistical
problems. However, when applied to modern large-scale models, it could face substantial …

Discrete-event simulation models generate random variates from input distributions and
compute outputs according to the simulation logic. The input distributions are typically fitted …

The bootstrap is a popular and powerful method for assessing precision of estimators and
inferential methods. However, for massive datasets that are increasingly prevalent, the …

We describe algorithms for exact computation of nonparametric bootstrap estimators, and
show that they are practicable for small samples. It is argued that in this setting, enumeration …

We consider the performance of the bootstrap in high-dimensions for the setting of linear
regression, where p< n but p/n is not close to zero. We consider ordinary least-squares as …

Efficient bootstrap simulation Page 151 EFFICIENT BOOTSTRAP SIMULATION Peter Hall¹
Australian National University Abstract. We survey a variety of methods for efficient …

Thebootstrap is by now a standard method in modern statistics. Its roots go back to a lot
ofresampling ideas that were around in the seventies. The seminal work of Efron …

The bootstrap provides a simple and powerful means of assessing the quality of estimators.
However, in settings involving large data sets—which are increasingly prevalent—the …

Bootstrap resampling methods have emerged as powerful tools for constructing inferential
procedures in modern statistical data analysis. Although these methods depend on the …

We consider bootstrap inference for estimators which are (asymptotically) biased. We show
that, even when the bias term cannot be consistently estimated, valid inference can be …