Resampling methods with applications

lstat2180  2021-2022  Louvain-la-Neuve

Resampling methods with applications
4.00 credits
15.0 h + 5.0 h
The class is focused on the presentation of key concepts based on resampling methods such as:
  • Basic ideas of bootstrap
  • Monte-Carlo methods
  • Applications to certain basic problems in estimation and inference
  • Bias/variance of an estimator 
  • Confidence intervals
  • Hypothesis testing based on resampling
  • Theoretical properties of bootstraap
  • Bootstrap for regression
  • Iterated bootstraap
  • The jackknife
  • The "smoothed" bootstrap
  • Bootstrap for time series models
Teaching methods
The class consists of lectures (15h) and exercises sessions (5h).
The classes and the TP are intended to be face to face.
Teaching language: English.
Evaluation methods
The evaluation for this course consists of three parts:
  • During the semester, the student must hand-in 2 compulsory assignments (short, 1 to 2 pages maximum per assignment), counting for 20% of the final grade. The homework is to be solved individually or in groups of 2. A grade will be awarded per group.
  • A project (written in French / English in min 5 and max 9 pages in the template on Moodle, annexes not included) which will illustrate the bootstrap method in a concrete case (30% of the points). The project is evaluated on the basis of the written report. The project is to be solved individually or in groups of 2. A score will be awarded per group.
  • An oral exam (~ 45 min.) at which the lecturer will assess the knowledge of the student with respect to the materials covered during the class (50% of the points). If necessary the lecturer will also ask questions about the results and the methodology used for the report and for the homework.
The exact evaluation methods could be adapted according to the constraints linked to the sanitary conditions in force at the time of the exam sessions. 
Online resources
Moodle website of the class: LSTAT2180 - Méthodes de rééchantillonnage avec applications.
  • Chernick, M.R. (2008). Bootstrap methods : a guide for practitioners and researchers, Wiley Series in Probability and Statistics.
  • Davison, A.C. et Hinkley, D.V. (1997). Bootstrap Methods and their Applications, Cambridge University Press.
  • Efron, B. et Tibshirani, R.J. (1993). An Introduction to the Bootstrap, Chapman and Hall.
  • Hall, P. (1992). The Bootstrap and Edgeworth Expansion, Springer.
  • Mammen, E. (1992). When does bootstrap work ? Springer.
Teaching materials
  • Transparents du cours et syllabus disponible sur Moodle
  • Notes de cours : Simar, L. (2008). An Invitation to the Bootstrap : Panacea for Statistical Inference ?
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Statistics: General

Master [120] in Statistics: Biostatistics

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Mathematical Engineering

Master [120] in Data Science : Statistic