Bootstrap Methods (eBook)

Michael R. Chernick, PhD, is Principal Senior Statistician at United BioSource 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).

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.

"Like the first edition, this edition can be used as useful
resource book for an array of purposes in various disciplines."
(Technometrics, November 2008)

"It is sufficiently well research that most bootstrapping
novices will get a good introduction to the subject and many
researchers will find something new to augment their knowledge."
(Biometrics, September 2008).

"With a strong focus on application ... and coverage of
modern developments ... [it] is indispensible for
...practitioners who regularly use statistical methods in
research." (Mathematical Reviews 2008)