When Does Bootstrap Work?: Asymptotic Results and Simulations: Lecture Notes in Statistics, cartea 77

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.