Some modifications and generalizations of the bootstrap procedurehave been proposed. In this note we will consider the wild bootstrap and the generalized bootstrap and we will give two arguments why it makes sense touse these modifications instead of the original bootstrap. The firstargument is that there exist examples where generalized and wild bootstrapwork, but where the original bootstrap fails and breaks down. The secondargument will be based on higher order considerations. We will show thatthe class of generalized and wild bootstrap procedures offers a broadspectrum of possibilities for adjusting higher order properties of the bootstrap
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The wild bootstrap is studied in the context of regression models with heteroskedastic disturbances....
It is well known that the standard i.i.d. bootstrap of the mean is inconsistent in a location model ...
Pre-print; version dated June 2006We consider how to select the auxiliary distribution to implement ...
none2noIn this paper we study the performance of the most popular bootstrap schemes for multilevel d...
Abstract. Bootstrap ideas yield remarkably effective algorithms for realizing certain pro-grams in s...
Consider a finite population $u$, which can be viewed as a realization of a superpopulation model. A...
Chapter One: The Truncated Wild Bootstrap for the Asymmetric Infinite Variance Case The wild bootstr...
Bootstrap, introduced by Efron (1979), is a general method of estimating the distribution Gn of a fu...
We introduce a wild bootstrap algorithm for the approximation of the sampling distribution of pair o...
The exchangeably weighted bootstrap is one of the many variants of bootstrap resampling schemes. Rat...
In this paper, we propose a model-free bootstrap method for the empirical process under absolute re...
In this paper, we propose bootstrap methods for statistics evaluated on high frequency data such as ...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The wild bootstrap is studied in the context of regression models with heteroskedastic disturbances....
It is well known that the standard i.i.d. bootstrap of the mean is inconsistent in a location model ...
Pre-print; version dated June 2006We consider how to select the auxiliary distribution to implement ...
none2noIn this paper we study the performance of the most popular bootstrap schemes for multilevel d...
Abstract. Bootstrap ideas yield remarkably effective algorithms for realizing certain pro-grams in s...
Consider a finite population $u$, which can be viewed as a realization of a superpopulation model. A...
Chapter One: The Truncated Wild Bootstrap for the Asymmetric Infinite Variance Case The wild bootstr...
Bootstrap, introduced by Efron (1979), is a general method of estimating the distribution Gn of a fu...
We introduce a wild bootstrap algorithm for the approximation of the sampling distribution of pair o...
The exchangeably weighted bootstrap is one of the many variants of bootstrap resampling schemes. Rat...
In this paper, we propose a model-free bootstrap method for the empirical process under absolute re...
In this paper, we propose bootstrap methods for statistics evaluated on high frequency data such as ...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
We propose a wild bootstrap procedure for linear regression models estimated by instrumental variabl...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...