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Topics

The slides in this page are organised in 7 chapters that cover all the topics in the course ``computer intensive methods using R: an introduction'' or “Basic Skills in Bootstrap using R”. The focus of this course is skills and not only the theory behind. In other words,  we focus on the question "How to do it ?" and not just of the question "why to do it ?". The approach we take in the course is to program the bootstrap procedure(s) and not  just to use R functions/packages to produce the results.  All examples are illustrated using the R software. 

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Be sure that you are familiar with the concept of a for loop in R.

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The course in slides format

Sampling from a population
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This chapter covers the following topics:

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  • The accuracy of the sample mean.

  • Random sampling.

  • The empirical distribution function and the plug-in principle.

  • Standard errors and estimated standard error.

The basic bootstrap
This chapter covers the following topics:

  • The basic bootstrap algorithm.

  • Parametric and non parametric bootstrap.

  • The bootstrap estimate of the standard error for the mean.

  • The correlation coefficient.

Bootstrap confidence intervals

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This chapter covers the topic of bootstrap confidence intervals:

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  • Bootstrap t intervals.

  • Percentile intervals.

  • The BCa method .

Bootstrap tests

 

This chapter is focused on inference and covers the following topics:

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  • Bootstrap tests: one sample problem and two samples problem.

  • Bootstrap tests for correlation and ratios.

  • Permutation  tests: two samples problem.

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Modeling

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This chapter is focused in statistical modelling and covers the topics of liner regression models and GLMs. In both cases estimation, confidence intervals and inference are discussed. We discuss non-parametric, parametric and semi-parametric methods.​
 

Selected topics

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The chapter covers the following topics:

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  • Estimation of bias (with both bootstrap and jackknife).

  • Cross-validation of prediction error.

  • The Jackknife.

Non parametric regression

 

This chapter covers the application of bootstrap methods when non parametric regression models are used.

R program

 

The R program includes the code for all the examples presented in the slides.
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