| Course label : | Statistics 2 |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S2_ST2 - Statistics 2 |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
• Estimators: bias, variance. Consistency, bias-variance decomposition. • Likelihood and maximum likelihood estimators. Exponential families. • Fisher information, Cramer-Rao bound lower bound on the variance of an unbiased estimator. • Asymptotic normality, Delta-method. • Asymptotic properties of the maximum likelihood estimators and associated tests • Likelihood-ratio tests, Uniformly more powerful tests • Mixture models, EM algorithm. All methods will be illustrated in practical sessions using Python.
Educational goals
After successfully taking this course, a student should be able to: • master the techniques of mathematical statistics.
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Final Exam, grading scale: (min) 0 – 20 (max)
Labs, grading scale: (min) 0 – 20 (max)
The final grade is the average of the exam and the labs grades. Passing grade is 10/20.
Online resources
1. Larry Wasserman, All of Statistics, A concise course in statistical inference.Springer, 2003. 2. A.Van der Vaart, Asymptotic Statistics. Cambridge University Press, 1998. 3. Vincent Rivoirard and Gilles Stoltz, Statistique en Action. Vuibert, 2009.
Pedagogy
24 hours, 6 lectures, 4 exercise sessions, 2 labs Language of instruction is specified in the course offering information in the course and programme directory. English is the default language.
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 12 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Probability 1. Statistics 1.