A practical and accessible introduction to the bootstrap
method–-newly revised and updated
Over the past decade, the application of bootstrap methods to
new areas of study has expanded, resulting in theoretical and
applied advances across various fields. Bootstrap Methods,
Second Edition is a highly approachable guide to the
multidisciplinary, real-world uses of bootstrapping and is ideal
for readers who have a professional interest in its methods, but
are without an advanced background in mathematics.
Updated to reflect current techniques and the most up-to-date
work on the topic, the Second Edition features:
* The addition of a second, extended bibliography devoted solely
to publications from 1999-2007, which is a valuable
collection of references on the latest research in the field
* A discussion of the new areas of applicability for bootstrap
methods, including use in the pharmaceutical industry for
estimating individual and population bioequivalence in clinical
trials
* A revised chapter on when and why bootstrap fails and remedies
for overcoming these drawbacks
* Added coverage on regression, censored data applications,
P-value adjustment, ratio estimators, and missing data
* New examples and illustrations as well as extensive historical
notes at the end of each chapter
With a strong focus on application, detailed explanations of
methodology, and complete coverage of modern developments in the
field, Bootstrap Methods, Second Edition is an indispensable
reference for applied statisticians, engineers, scientists,
clinicians, and other practitioners who regularly use statistical
methods in research. It is also suitable as a supplementary text
for courses in statistics and resampling methods at the
upper-undergraduate and graduate levels.
Cuprins
Preface to Second Edition.
Preface to First Edition.
Acknowledgments.
1. What Is Bootstrapping?
1.1. Background.
1.2. Introduction.
1.3. Wide Range of Applications.
1.4. Historical Notes.
1.5. Summary.
2. Estimation.
2.1. Estimating Bias.
2.2. Estimating Location and Dispersion.
2.3. Historical Notes.
3. Confi dence Sets and Hypothesis Testing.
3.1. Confi dence Sets.
3.2. Relationship Between Confi dence Intervals and Tests of
Hypotheses.
3.3. Hypothesis Testing Problems.
3.4. An Application of Bootstrap Confi dence Intervals to Binary
Dose-Response Modeling.
3.5. Historical Notes.
4. Regression Analysis.
4.1. Linear Models.
4.2. Nonlinear Models.
4.3. Nonparametric Models.
4.4. Historical Notes.
5. Forecasting and Time Series Analysis.
5.1. Methods of Forecasting.
5.2. Time Series Models.
5.3. When Does Bootstrapping Help with Prediction Intervals?
5.4. Model-Based Versus Block Resampling.
5.5. Explosive Autoregressive Processes.
5.6. Bootstrapping-Stationary Arma Models.
5.7. Frequency-Based Approaches.
5.8. Sieve Bootstrap.
5.9. Historical Notes.
6. Which Resampling Method Should You Use?
6.1. Related Methods.
6.2. Bootstrap Variants.
7. Effi cient and Effective Simulation.
7.1. How Many Replications?
7.2. Variance Reduction Methods.
7.3. When Can Monte Carlo Be Avoided?
7.4. Historical Notes.
8. Special Topics.
8.1. Spatial Data.
8.2. Subset Selection.
8.3. Determining the Number of Distributions in a Mixture
Model.
8.4. Censored Data.
8.5. p-Value Adjustment.
8.6. Bioequivalence Applications.
8.7. Process Capability Indices.
8.8. Missing Data.
8.9. Point Processes.
8.10. Lattice Variables.
8.11. Historical Notes.
9. When Bootstrapping Fails Along with Remedies for
Failures.
9.1. Too Small of a Sample Size.
9.2. Distributions with Infi nite Moments.
9.3. Estimating Extreme Values.
9.4. Survey Sampling.
9.5. Data Sequences that Are M-Dependent.
9.6. Unstable Autoregressive Processes.
9.7. Long-Range Dependence.
9.8. Bootstrap Diagnostics.
9.9. Historical Notes.
Bibliography 1 (Prior to 1999).
Bibliography 2 (1999-2007).
Author Index.
Subject Index.
Despre autor
Michael R. Chernick, Ph D, is Principal Senior Statistician at United Bio Source Corporation, providing statistical design and analysis for pharmaceutical research on a variety of diseases, including a recent emphasis on oncology. He has over twenty years of experience in the application of statistical methods to the pharmaceutical industry as well as to medical devices, energy data, engineering problems, and insurance data. His research interests include extreme value theory, outliers, data editing, time series, and bootstrap methods. Dr. Chernick is also the coauthor of Introductory Biostatistics for the Health Sciences (Wiley).
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