When Does Bootstrap Work?: Asymptotic Results and Simulations
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 Boo...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Dokumenttyp: | Buch/Monographie |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
Springer New York
1992
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| Schriftenreihe: | Lecture Notes in Statistics
77 SpringerLink Bücher Springer eBook Collection Mathematics and Statistics |
| DOI: | 10.1007/978-1-4612-2950-6 |
| Schlagworte: | |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/978-1-4612-2950-6 Verlag, Zentralblatt MATH, Inhaltstext: https://zbmath.org/?q=an:0760.62038 
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| Verfasserangaben: | by Enno Mammen |
MARC
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| 520 | |a 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 -- 3. Proofs -- 4. A Bootstrap Test on the Number of Modes of a Density -- 1. Introduction -- 2. The number of modes of a kernel density estimator -- 3. Bootstrapping the test statistic -- 4. Proofs -- Figures -- 5. Higher-Order Accuracy of Bootstrap for Smooth Functionals -- 1. Introduction -- 2. Bootstrapping smooth functionals -- 3. Some more simulations. Bootstrapping an M-estimate -- 4. Proof of the theorem -- Figures -- 6. Bootstrapping Linear Models -- 1. Bootstrapping the least squares estimator -- 2. Bootstrapping F-tests -- 3. Proof of Theorem 3 -- 7. Bootstrapping Robust Regression -- 1. Introduction -- 2. Bootstrapping M-estimates -- 3. Stochastic expansions of M-estimates -- 4. Proofs -- Figures -- 8. Bootstrap and wild Bootstrap for High-Dimensional Linear Random Design Models -- 1. Introduction -- 2. Consistency of bootstrap for linear contrasts -- 3. Accuracy of the bootstrap -- 4. Bootstrapping F-tests -- 5. Proofs -- Tables -- Figures -- 9. References. | ||
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