Advanced Statistical Methods
Syllabus
This course aims first at introducing the general methodology of mathematical statistics through the fundamental concepts (statistical modeling and sampling, estimation problems, decision theory, and hypothesis testing). Then, this course provides advanced statistical techniques for multivariate analysis with a particular focus on computational statistics and robust estimation approaches. Regularized/penalized techniques are also presented.
Outline 2021/2022
- Introduction [Slides]
- Part A: Reminders of Probability Theory and Mathematical Statistics - Focus on Extreme Value Theory [Slides]
Statistical models and estimation
- Part B1: Statistical Modelling and Parameter Estimation Theory [Slides]
- Part B2: Computational Statistics [Slides]
- Part B3: Applications [Slides]
Hypothesis testing and decision theory
- Part C1: Hypothesis Testing - Decision Theory [Slides]
- Part C2: Applications [Slides]
Robust estimation theory
- Part D: Robust Statistics [Slides]
- Part E: Applications to Data Processing
Materials and proofs (2020/2021)
- Some proofs are available here [Proofs]
- Support for the course of probabilities (in french) [.pdf]
Lab Sessions (LB) 2021/2022
Exam 2021/2022
Exam 2020/2021
Exam 2019/2020
Exam 2018/2019
