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This course presents the mathematical theory of statistical inference. It deepens and completes the Statistical Principles and Methods course.
1. Concepts of statistical inference. Statistical model. Likelihood. Sufficiency.
2. Optimal parametric estimation. Minimum variance unbiased estimation. Fisher Information.
3. Maximum likelihood estimation. Likelihood ratio tests.
4. Nonparametric estimation. Order and rank statistics.
5. Extreme values statistics.
6. Functional estimation.
7. Goodness-of-fit tests.
Probability Theory and Applications, Statistical Principles and Methods (first year).
Written exam (3 hours, documents allowed) (E).
N1 = E1
N2 = E2
The course exists in the following branches:
Course ID : 4MMSIA6
Course language(s):
The course is attached to the following structures:
You can find this course among all other courses.
Polycopié de cours.
P. EMBRECHTS, C. KLÜPPELBERG, T. MIKOSCH : Modelling extremal events, Springer, 1997.
D. FOURDRINIER : Statistique inférentielle, Dunod, 2002.
A. MONFORT : Cours de Statistique Mathématique, Economica, 1997.
J.A. RICE : Mathematical Statistics and Data Analysis, Duxbury Press, 2006.
G. SAPORTA : Probabilités, analyse des données et statistique, Technip, 2011.
J. SHAO : Mathematical Statistics, Springer, 2003.
P. TASSI : Méthodes statistiques, Economica, 2004.
Date of update January 15, 2017
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