Resampling methods with applications

lstat2180  2020-2021  Louvain-la-Neuve

Resampling methods with applications
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
4 credits
15.0 h + 5.0 h
Q1
Teacher(s)
Language
French
Content
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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The class consists of lectures (15h) and exercises sessions (5h).
Teaching language: English.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

An oral examination, where the instructors evaluate: 
  • knowledge about the concepts seen in class throughout the semester  (50% des points);
  • the quality of a project (written in French / English in min 5 and max 8 pages in the template on Moodle, annexes not included) of data analysis/simulation that ilustrates the bootstraap methods in a concrete case  (50% des points). This written project will be handed in before the exam session and discussed with the instructors during the exam session. The evaluation of the project is based on the written manuscript and responses to questions in an oral discussion about the results and the methodology used for the report.
The failure of one of the two parts results in the automatic failure of the course!
To be allowed to take part in the examination the student has to submit 3 compulsory homeworks (short, 1-2 pages maximum per homework). The homeworks are not graded as they are not part of the evaluation.
Submission of less than 3 homework results in failure of the course!
Online resources
Moodle website of the class: LSTAT2180 - Méthodes de rééchantillonnage avec applications.
https://moodleucl.uclouvain.be/course/view.php?id=8140
Bibliography
  • 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
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic

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

Master [120] in Mathematical Engineering

Master [120] in Statistic: General

Master [120] in Statistic: Biostatistics