An Introduction to Bootstrap Methods with Applications to R

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Detalles Bibliográficos
Autor principal: Chernick, Michael R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2011.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • List of Tables
  • 1: INTRODUCTION
  • 1.1 Historical Background
  • 1.2 Definition and Relationship to the Delta Method and Other Resampling Methods
  • 1.2.1 Jackknife
  • 1.2.2 Delta Method
  • 1.2.3 Cross Validation
  • 1.2.4 Subsampling
  • 1.3 Wide Range of Applications
  • 1.4 The Bootstrap and the R Language System
  • 1.5 Historical Notes
  • 1.6 Exercises
  • References
  • 2: ESTIMATION
  • 2.1 Estimating Bias
  • 2.1.1 Bootstrap Adjustment
  • 2.1.2 Error Rate Estimation in Discriminant Analysis
  • 2.1.3 Simple Example of Linear Discrimination and Bootstrap Error Rate Estimation
  • 2.1.4 Patch Data Example
  • 2.2 Estimating Location
  • 2.2.1 Estimating a Mean
  • 2.2.2 Estimating a Median
  • 2.3 Estimating Dispersion
  • 2.3.1 Estimating an Estimate's Standard Error
  • 2.3.2 Estimating Interquartile Range
  • 2.4 Linear Regression
  • 2.4.1 Overview
  • 2.4.2 Bootstrapping Residuals
  • 2.4.3 Bootstrapping Pairs (response and Predictor Vector)
  • 2.4.4 Heteroscedasticity of Variance: the Wild Bootstrap
  • 2.4.5 a Special Class of Linear Regression Models: Multivariable Fractional Polynomials
  • 2.5 Nonlinear Regression
  • 2.5.1 Examples of Nonlinear Models
  • 2.5.2 a Quasi Optical Experiment
  • 2.6 Nonparametric Regression
  • 2.6.1 Examples of Nonparametric Regression Models
  • 2.6.2 Bootstrap Bagging
  • 2.7 Historical Notes
  • 2.8 Exercises
  • References
  • 3: CONFIDENCE INTERVALS
  • 3.1 Subsampling, Typical Value Theorem, and Efron's Percentile Method
  • 3.2 Bootstrap-t
  • 3.3 Iterated Bootstrap
  • 3.4 Bias Corrected (BC) Bootstrap
  • 3.5 Bca and Abc
  • 3.6 Tilted Bootstrap
  • 3.7 Variance Estimation with Small Sample Sizes
  • 3.8 Historical Notes
  • 3.9 Exercises
  • References
  • 4: HYPOTHESIS TESTING
  • 4.1 Relationship to Confidence Intervals
  • 4.2 Why Test Hypotheses Differently?
  • 4.3 Tendril Dx Example
  • 4.4 Klingenberg Example: Binary Dose-response
  • 4.5 Historical Notes
  • 4.6 Exercises
  • References
  • 5: TIME SERIES
  • 5.1 Forecasting Methods
  • 5.2 Time Domain Models
  • 5.3 Can Bootstrapping Improve Prediction Intervals?
  • 5.4 Model Based Methods
  • 5.4.1 Bootstrapping Stationary Autoregressive Processes
  • 5.4.2 Bootstrapping Explosive Autoregressive Processes
  • 5.4.3 Bootstrapping Unstable Autoregressive Processes
  • 5.4.4 Bootstrapping Stationary Arma Processes
  • 5.5 Block Bootstrapping for Stationary Time Series
  • 5.6 Dependent Wild Bootstrap (DWB)
  • 5.7 Frequency-based Approaches for Stationary Time Series
  • 5.8 Sieve Bootstrap
  • 5.9 Historical Notes
  • 5.10 Exercises
  • References
  • 6: BOOTSTRAP VARIANTS
  • 6.1 Bayesian Bootstrap
  • 6.2 Smoothed Bootstrap
  • 6.3 Parametric Bootstrap
  • 6.4 Double Bootstrap
  • 6.5 the M-out-of-n Bootstrap
  • 6.6 the Wild Bootstrap
  • 6.7 Historical Notes
  • 6.8 Exercise
  • References
  • 7: CHAPTER SPECIAL TOPICS
  • 7.1 Spatial Data
  • 7.1.1 Kriging

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