This note illustrates, through a very simple example, the power and flexibility of the bootstrap method to assess in a straightforward and unambiguous way how the random errors in direct measurements propagate themselves into the derived quantities of interest. Furthermore, a version of the bootstrap with the initial data set generated by Monte Carlo has proved to be suitable in situations where the standard deviations of the directly measured quantities may be considered as known, but the effect of various experimental errors on the accuracy of final results is quite obscure
When measurement error is present among the covariates of a regression model it can cause bias in th...
We first propose two procedures for estimating the rejection probabilities of bootstrap tests in Mon...
This chapter describes the set up step series, developed by the Genoa Research Group on Production S...
This paper deeals with an applications of the bootstrap method to the error analysis when the data c...
Bootstrapping is a nonparametric approach for evaluating the distribution of a statistic based on ra...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
The Monte Carlo simulation is a commonly used technique for circuit analysis, but is computationally...
The application of bootstrap approach allows the estimations of the measurements of precision and ca...
We introduce the concept of the bootstrap discrepancy, which measures the di#erence in rejection pro...
The bootstrap method is a powerful technique to evaluate the statistical uncertainty of a measuremen...
Abstract. Bootstrap ideas yield remarkably effective algorithms for realizing certain pro-grams in s...
In this paper we discuss three methods to apply the bootstrap correctly to hypothesis testing. For e...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
This paper provides a method for determining the exact finite sample properties of the bootstrap. Pr...
This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap sta...
When measurement error is present among the covariates of a regression model it can cause bias in th...
We first propose two procedures for estimating the rejection probabilities of bootstrap tests in Mon...
This chapter describes the set up step series, developed by the Genoa Research Group on Production S...
This paper deeals with an applications of the bootstrap method to the error analysis when the data c...
Bootstrapping is a nonparametric approach for evaluating the distribution of a statistic based on ra...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
The Monte Carlo simulation is a commonly used technique for circuit analysis, but is computationally...
The application of bootstrap approach allows the estimations of the measurements of precision and ca...
We introduce the concept of the bootstrap discrepancy, which measures the di#erence in rejection pro...
The bootstrap method is a powerful technique to evaluate the statistical uncertainty of a measuremen...
Abstract. Bootstrap ideas yield remarkably effective algorithms for realizing certain pro-grams in s...
In this paper we discuss three methods to apply the bootstrap correctly to hypothesis testing. For e...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
This paper provides a method for determining the exact finite sample properties of the bootstrap. Pr...
This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap sta...
When measurement error is present among the covariates of a regression model it can cause bias in th...
We first propose two procedures for estimating the rejection probabilities of bootstrap tests in Mon...
This chapter describes the set up step series, developed by the Genoa Research Group on Production S...