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Inhaltsverzeichnis
- 0. Introduction.
- 1. Bootstrap and Asymptotic Normality.
- 1. Introduction.
- 2. Bootstrapping linear functionals. The i. i. d. case.
- 3. Bootstrapping smooth functionals.
- 4. Bootstrap and wild bootstrap in non i. i. d. models.
- 5. Some simulations.
- 6. Proofs.
- Figures.
- 2. An Example Where Bootstrap Fails: Comparing Nonparametric Versus Parametric Regression Fits.
- 1. A goodness-of-fit test.
- 2. How to bootstrap. Bootstrap and wild bootstrap.
- 3. Proofs.
- 3. A Bootstrap Success Story: Using Nonparametric Density Estimates in K-Sample Problems.
- 1. Bootstrap tests.
- 2. Bootstrap confidence regions.
- 4. A Bootstrap Test on the Number of Modes of a Density.
- 2. The number of modes of a kernel density estimator.
- 3. Bootstrapping the test statistic.
- 4. Proofs.
- 5. Higher-Order Accuracy of Bootstrap for Smooth Functionals.
- 2. Bootstrapping smooth functionals.
- 3. Some more simulations. Bootstrapping an M-estimate.
- 4. Proof of the theorem.
- 6. Bootstrapping Linear Models.
- 1. Bootstrapping the least squares estimator.
- 2. Bootstrapping F-tests.
- 3. Proof of Theorem 3.
- 7. Bootstrapping Robust Regression.
- 2. Bootstrapping M-estimates.
- 3. Stochastic expansions of M-estimates.
- 8. Bootstrap and wild Bootstrap for High-Dimensional Linear Random Design Models.
- 2. Consistency of bootstrap for linear contrasts.
- 3. Accuracy of the bootstrap.
- 4. Bootstrapping F-tests.
- 5. Proofs.
- Tables.
- 9. References.