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).
Gradient-based global sensitivity analysis – Master M2RI 2023
- Slides
- Computer lab: flood_notebook_M2RI (html version). Download the R code here
Principal component analysis and applications
- Slides
- Applets (with Shiny)
Bootstrap, Bagging, Random forests
- Slides
- R codes
Introduction to statistical learning (slides, 3h)
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
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
GARCH model: Application to volatility forecasting (slides in French)
Stochastic processes, martingales, brownian motion and stochastic calculus (in French)
- Stochastic processes – Generality and examples
- Textbook on martingales
- List of exercices