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Introduction to Bootstrap Methods with Applications to R - Info and Reading Options

"Introduction to Bootstrap Methods with Applications to R" was published by Wiley & Sons, Incorporated, John in 2014 - Newark, it has 240 pages and the language of the book is English.


“Introduction to Bootstrap Methods with Applications to R” Metadata:

  • Title: ➤  Introduction to Bootstrap Methods with Applications to R
  • Authors:
  • Language: English
  • Number of Pages: 240
  • Publisher: ➤  Wiley & Sons, Incorporated, John
  • Publish Date:
  • Publish Location: Newark

“Introduction to Bootstrap Methods with Applications to R” Subjects and Themes:

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"Introduction to Bootstrap Methods with Applications to R" Description:

Open Data:

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

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