The exchangeably weighted bootstrap is one of the many variants of bootstrap resampling schemes. Rather than directly drawing observations with replacement from the data, weighted bootstrap schemes generate vectors of replication weights to form bootstrap replications. Various ways to generate the replication weights can be adopted, and some choices bring practical computational advantages. This presentation demonstrates how easily such schemes can be implemented and where they are particularly useful, and introduces the exbsample command, which facilitates their implementation
The bootstrap is a powerful non-parametric statistical technique for making probability-based infere...
Many simulation experiments have shown that, in a variety of circumstances, bootstrap tests perform ...
It is known that Efron's resampling bootstrap of the mean of random variables with common distributi...
We illustrate bootstrap methods in a simple example, Among ideas discussed are: basic distributional...
The Bootstrap is the most widely used resampling statistical method. This method becomes very popula...
The problem of number of parametric bootstrap replications that are necessary to realizace at bias e...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
The bootstrap is a statistical technique used more and more widely in econometrics. While it is capa...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The operation of resampling from a bootstrap resample, encountered in applications of the double boo...
The introduction of the bootstrap methods by Efron (1979) enables many empirical researches, which w...
Two procedures are proposed for estimating the rejection probabilities of bootstrap tests in Monte C...
A new and very fast method of bootstrap for sampling without replacement from a finite population is...
Sampling is one of the most fundamental concepts in statistics, as the quality and accuracy of the s...
[[abstract]]Often when dealing with complex data structures there is no unique way to bootstrap. If ...
The bootstrap is a powerful non-parametric statistical technique for making probability-based infere...
Many simulation experiments have shown that, in a variety of circumstances, bootstrap tests perform ...
It is known that Efron's resampling bootstrap of the mean of random variables with common distributi...
We illustrate bootstrap methods in a simple example, Among ideas discussed are: basic distributional...
The Bootstrap is the most widely used resampling statistical method. This method becomes very popula...
The problem of number of parametric bootstrap replications that are necessary to realizace at bias e...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
The bootstrap is a statistical technique used more and more widely in econometrics. While it is capa...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The operation of resampling from a bootstrap resample, encountered in applications of the double boo...
The introduction of the bootstrap methods by Efron (1979) enables many empirical researches, which w...
Two procedures are proposed for estimating the rejection probabilities of bootstrap tests in Monte C...
A new and very fast method of bootstrap for sampling without replacement from a finite population is...
Sampling is one of the most fundamental concepts in statistics, as the quality and accuracy of the s...
[[abstract]]Often when dealing with complex data structures there is no unique way to bootstrap. If ...
The bootstrap is a powerful non-parametric statistical technique for making probability-based infere...
Many simulation experiments have shown that, in a variety of circumstances, bootstrap tests perform ...
It is known that Efron's resampling bootstrap of the mean of random variables with common distributi...