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Bootstrap methods are among the most active areas of statistical research, and are important to those interested in statistical theory and applications. This ground-breaking reference remains a basic reference in this important area.
Bootstrap methods are among the most active areas of statistical research, and are important to those interested in statistical theory and applications. This ground-breaking reference remains a basic reference in this important area.
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Inhaltsangabe
1: Principles of Bootstrap Methodology. 2: Principles of Edgeworth Expansion. 3: An Edgeworth View of the Bootstrap. 4: Bootstrap Curve Estimation. 5: Details of Mathematical Rigour. Appendix I: Number and Sizes of Atoms of Nonparametric Bootstrap Distribution. Appendix II: Monte Carlo Simulation. II.1 Introduction. II.2 Uniform Resampling. II.3 Linear Approximation. II.4 Centring Method. II.5 Balanced Resampling. II.6 Antithetic Resampling. II.7 Importance Resampling. II.7.1 Introduction. II.7.2 Concept of Importance Resampling. II.7.3 Importance Resampling for Approximating Bias, Variance, Skewness, etc.. II.7.4 Importance Resampling for a Distribution Function. II.8 Quantile Estimation. Appendix III: Confidence Pictures. Appendix IV: A Non Standard Example: Quantite Error Estimation. IV. 1 Introduction. IV.2 Definition of the Mean Squared Error Estimate. IV.3 Convergence Rate of the Mean Squared Error Estimate. IV.4 Edgeworth Expansions for the Studentized Bootstrap Quantile Estimate. Appendix V: A Non Edgeworth View of the Bootstrap. References. Author Index.
1: Principles of Bootstrap Methodology.- 2: Principles of Edgeworth Expansion.- 3: An Edgeworth View of the Bootstrap.- 4: Bootstrap Curve Estimation.- 5: Details of Mathematical Rigour.- Appendix I: Number and Sizes of Atoms of Nonparametric Bootstrap Distribution.- Appendix II: Monte Carlo Simulation.- II.1 Introduction.- II.2 Uniform Resampling.- II.3 Linear Approximation.- II.4 Centring Method.- II.5 Balanced Resampling.- II.6 Antithetic Resampling.- II.7 Importance Resampling.- II.7.1 Introduction.- II.7.2 Concept of Importance Resampling.- II.7.3 Importance Resampling for Approximating Bias, Variance, Skewness, etc..- II.7.4 Importance Resampling for a Distribution Function.- II.8 Quantile Estimation.- Appendix III: Confidence Pictures.- Appendix IV: A Non-Standard Example: Quantite Error Estimation.- IV. 1 Introduction.- IV.2 Definition of the Mean Squared Error Estimate.- IV.3 Convergence Rate of the Mean Squared Error Estimate.- IV.4 Edgeworth Expansions for the Studentized Bootstrap Quantile Estimate.- Appendix V: A Non-Edgeworth View of the Bootstrap.- References.- Author Index.
1: Principles of Bootstrap Methodology. 2: Principles of Edgeworth Expansion. 3: An Edgeworth View of the Bootstrap. 4: Bootstrap Curve Estimation. 5: Details of Mathematical Rigour. Appendix I: Number and Sizes of Atoms of Nonparametric Bootstrap Distribution. Appendix II: Monte Carlo Simulation. II.1 Introduction. II.2 Uniform Resampling. II.3 Linear Approximation. II.4 Centring Method. II.5 Balanced Resampling. II.6 Antithetic Resampling. II.7 Importance Resampling. II.7.1 Introduction. II.7.2 Concept of Importance Resampling. II.7.3 Importance Resampling for Approximating Bias, Variance, Skewness, etc.. II.7.4 Importance Resampling for a Distribution Function. II.8 Quantile Estimation. Appendix III: Confidence Pictures. Appendix IV: A Non Standard Example: Quantite Error Estimation. IV. 1 Introduction. IV.2 Definition of the Mean Squared Error Estimate. IV.3 Convergence Rate of the Mean Squared Error Estimate. IV.4 Edgeworth Expansions for the Studentized Bootstrap Quantile Estimate. Appendix V: A Non Edgeworth View of the Bootstrap. References. Author Index.
1: Principles of Bootstrap Methodology.- 2: Principles of Edgeworth Expansion.- 3: An Edgeworth View of the Bootstrap.- 4: Bootstrap Curve Estimation.- 5: Details of Mathematical Rigour.- Appendix I: Number and Sizes of Atoms of Nonparametric Bootstrap Distribution.- Appendix II: Monte Carlo Simulation.- II.1 Introduction.- II.2 Uniform Resampling.- II.3 Linear Approximation.- II.4 Centring Method.- II.5 Balanced Resampling.- II.6 Antithetic Resampling.- II.7 Importance Resampling.- II.7.1 Introduction.- II.7.2 Concept of Importance Resampling.- II.7.3 Importance Resampling for Approximating Bias, Variance, Skewness, etc..- II.7.4 Importance Resampling for a Distribution Function.- II.8 Quantile Estimation.- Appendix III: Confidence Pictures.- Appendix IV: A Non-Standard Example: Quantite Error Estimation.- IV. 1 Introduction.- IV.2 Definition of the Mean Squared Error Estimate.- IV.3 Convergence Rate of the Mean Squared Error Estimate.- IV.4 Edgeworth Expansions for the Studentized Bootstrap Quantile Estimate.- Appendix V: A Non-Edgeworth View of the Bootstrap.- References.- Author Index.
Rezensionen
"...an up-to-date and comprehensive account of the title theme...provides a path to several major research frontiers in the bootstrap literature." - Journal of the American Statistical Association
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