UID:
almahu_9947362825902882
Umfang:
VI, 201 p.
,
online resource.
ISBN:
9781461229506
Serie:
Lecture Notes in Statistics, 77
Anmerkung:
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.
In:
Springer eBooks
Weitere Ausg.:
Printed edition: ISBN 9780387978673
Sprache:
Englisch
DOI:
10.1007/978-1-4612-2950-6
URL:
http://dx.doi.org/10.1007/978-1-4612-2950-6
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