Material

In this section you will find some teaching material: textbook, slides, R code…
Feel free to use / adapt them for your own needs (simply cite the source).

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Bootstrap, Bagging, Random forests

• Slides
  • Bootstrap, bagging, random forest
  • Confidence / Prediction intervals by bootstrap
• R codes
  • Illustration of bagging
  • Prevision interval by bootstrap in regression
  • Confidence/prediction interval by bootstrap for random forest

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Introduction to statistical learning (slides, 3h)

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Introduction to linear regression (slides in French, 9h)

Basics on linear regression with a small number of explanatory variables.

• course 1 : Introduction : definition, interpretation and relevancy
• course 2 : Estimation : least squares, maximum likelihood
• course 3 : Influence of one predictor : significance test, ANOVA table
• course 4 : Geometrical interpretation. Prediction performance and model validation

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Introduction to time series (textbook in French)

• Outline :
  • Descriptive statistics. Forecast by exponential smoothing
  • Probabilistic framework. SARIMA models. Box and Jenkins methodology
  • Forecast with a probabilistic model

• Link : website of the book “Forecasting: Methods and Applications” by Makridakis, WheelWright, Hyndman

• Support : R tutorial for time series

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GARCH model: Application to volatility forecasting (slides in French)

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Stochastic processes, martingales, brownian motion and stochastic calculus (in French)

• Stochastic processes – Generality and examples
• Textbook on martingales
• List of exercices