An Introduction to the Bootstrap

  title={An Introduction to the Bootstrap},
  author={Bradley Efron and Robert Tibshirani},
Bootstrapped inference for variance parameters, measures of heterogeneity and random effects in multilevel logistic regression models
We used Monte Carlo simulations to assess the performance of three bootstrap procedures for use with multilevel data (the parametric bootstrap, the residuals bootstrap, and the nonparametric
Statistical Research by Surveys : Case Studies , Constraints and Particularities 188 BOOTSTRAP AND JACKKNIFE RESAMPLING ALGORITHMS FOR ESTIMATION OF REGRESSION PARAMETERS
In this paper, the hierarchical ways for building a regression model by using bootstrap and jackknife resampling methods were presented. Bootstrap approaches based on the observations and errors
The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation
Dependent Bootstrap Confidence Intervals for a Population Mean
This study compares and analyzes the coverage probabilities and the average interval lengths of confidence interval for a population mean based on the dependent bootstrap procedure against those
Small-sample Confidence Intervals for Impulse Response Functions
  • L. Kilian
  • Economics, Mathematics
    Review of Economics and Statistics
  • 1998
Bias-corrected bootstrap confidence intervals explicitly account for the bias and skewness of the small-sample distribution of the impulse response estimator, while retaining asymptotic validity in
On Bootstrapping Using Smoothed Bootstrap
  • Sulafah Binhimd
  • Economics
    Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017)
  • 2019
The standard bootstrap method was introduced as a resampling method for statistical inference; it is a computer based method for assigning measures of accuracy to statistical estimates. The bootstrap
Bootstrap confidence intervals for the process capability index under half-logistic distribution
This study concerns the construction of bootstrap confidence intervals for the process capability index in the case of half-logistic distribution. The bootstrap confidence intervals applied consist
The construction of confidence intervals for frequency analysis using resampling techniques
Abstract. Resampling techniques such as the Bootstrap and the Jack-knife are generic methods for the estimation of uncertainties in statistics. When applied in frequency analysis, resampling
Efficient bootstrap estimates for tail statistics
Abstract. Bootstrap resamples can be used to investigate the tail of empirical distributions as well as return value estimates from the extremal behaviour of the sample. Specifically, the confidence
Weighted Bootstrap with Probability in Regression
In statistical inference we are often concerned in procuring the standard errors of the estimates of parameters and their confidence intervals. The t and the z statistics used to construct confidence