Bootstrap

General

• Bootstrap from Wikipedia.
  • This contains an overview of different methods for computing bootstrap confidence intervals.
  • boot.ci() from the 'boot' package provides a short explanation for different methods for computing bootstrap confidence intervals.
• Bootstrapping made easy and tidy with slipper
• bootstrap package. "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993
• boot package. Functions and datasets for bootstrapping from the book Bootstrap Methods and Their Application by A. C. Davison and D. V. Hinkley (1997, CUP). A short course material can be found here.The main functions are boot() and boot.ci().
  • https://www.rdocumentation.org/packages/boot/versions/1.3-20
  • R in Action Nonparametric bootstrapping

    # Compute the bootstrapped 95% confidence interval for R-squared in the linear regression
    rsq <- function(data, indices, formula) {
      d <- data[indices,] # allows boot to select sample
      fit <- lm(formula, data=d)
      return(summary(fit)$r.square)
    } # 'formula' is optional depends on the problem
    
    # bootstrapping with 1000 replications
    set.seed(1234)
    bootobject <- boot(data=mtcars, statistic=rsq, R=1000, 
                       formula=mpg~wt+disp)
    plot(bootobject) # or plot(bootobject, index = 1) if we have multiple statistics
    ci <- boot.ci(bootobject, conf = .95, type=c("perc", "bca") ) 
        # default type is "all" which contains c("norm","basic", "stud", "perc", "bca"). 
        # 'bca' (Bias Corrected and Accelerated) by Efron 1987 uses 
        # percentiles but adjusted to account for bias and skewness.
    # Level     Percentile            BCa          
    # 95%   ( 0.6838,  0.8833 )   ( 0.6344,  0.8549 )
    # Calculations and Intervals on Original Scale
    # Some BCa intervals may be unstable
    ci$bca[4:5]  
    # [1] 0.6343589 0.8549305
    # the mean is not the same
    mean(c(0.6838,  0.8833 ))
    # [1] 0.78355
    mean(c(0.6344,  0.8549 ))
    # [1] 0.74465
    summary(lm(mpg~wt+disp, data = mtcars))$r.square
    # [1] 0.7809306

  • Resampling Methods in R: The boot Package by Canty
  • An introduction to bootstrap with applications with R by Davison and Kuonen.
  • http://people.tamu.edu/~alawing/materials/ESSM689/Btutorial.pdf
  • http://statweb.stanford.edu/~tibs/sta305files/FoxOnBootingRegInR.pdf
  • http://www.stat.wisc.edu/~larget/stat302/chap3.pdf
  • https://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf. Variance, se, bias, confidence interval (basic, percentile), hypothesis testing, parametric & non-parametric bootstrap, bootstrapping regression models.
  • Understanding Bootstrap Confidence Interval Output from the R boot Package which covers the nonparametric and parametric bootstrap.
• http://www.math.ntu.edu.tw/~hchen/teaching/LargeSample/references/R-bootstrap.pdf No package is used
• http://web.as.uky.edu/statistics/users/pbreheny/621/F10/notes/9-21.pdf Bootstrap confidence interval
• http://www-stat.wharton.upenn.edu/~stine/research/spida_2005.pdf
• Optimism corrected bootstrapping (Harrell et al 1996)
  • Adjusting for optimism/overfitting in measures of predictive ability using bootstrapping
  • Part 1: Optimism corrected bootstrapping: a problematic method
  • Part 2: Optimism corrected bootstrapping is definitely bias, further evidence
  • Part 3: Two more implementations of optimism corrected bootstrapping show shocking bias
  • Part 4: Why does bias occur in optimism corrected bootstrapping?
  • Part 5: Code corrections to optimism corrected bootstrapping series
• Bootstrapping Part 2: Calculating p-values!!! from StatQuest
• Using bootstrapped sampling to assess variability in score predictions. The rsample (General Resampling Infrastructure) package was used.
• Chapter 8 Bootstrapping and Confidence Intervals from the ebook "Statistical Inference via Data Science"

Nonparametric bootstrap

This is the most common bootstrap method

The upstrap Crainiceanu & Crainiceanu, Biostatistics 2018

Parametric bootstrap

• Parametric bootstraps resample a known distribution function, whose parameters are estimated from your sample
• http://www.math.ntu.edu.tw/~hchen/teaching/LargeSample/notes/notebootstrap.pdf#page=3 No package is used
• A parametric or non-parametric bootstrap?
• https://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf#page=11
• simulatorZ Bioc package

Examples

Standard error

Standard error

foo <- function() mean(sample(x, replace = TRUE))
set.seed(1234)
x <- rnorm(300)
set.seed(1)
sd(replicate(10000, foo()))
# [1] 0.05717679
sd(x)/sqrt(length(x)) # The se of mean is s/sqrt(n)
# [1] 0.05798401

set.seed(1234)
x <- rpois(300, 2)
set.seed(1)
sd(replicate(10000, foo()))
# [1] 0.08038607
sd(x)/sqrt(length(x)) # The se of mean is s/sqrt(n)
# [1] 0.08183151

Bootstrapping Extreme Value Estimators

Bootstrapping Extreme Value Estimators de Haan, 2022