Checking date: 20/05/2021

Course: 2021/2022

Simulation and Resampling
Study: Master in Statistics for Data Science (345)

Coordinating teacher: CASCOS FERNANDEZ, IGNACIO

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Probability Statistical Inference
Knowledge acquisition of: 1) random variables, elementary probability and distributions; 2) relevant probabilistic inequalities; 3) random vectors, marginal and joint distributions; 4) sequences of random variables and concepts of convergences; 5) Markov chains; 6) Poisson processes; 7) processes in continuous time; 8) univariate and multivariate simulation methods; 9) non-parametric and parametric resampling methods.
Skills and learning outcomes
Description of contents: programme
1. Introduction to Monte Carlo techniques 2. Simulating random variables and vectos 3. Discrete event simulation 4. Variance reduction and MCMC 5. Introduction to the bootstrap 6. Bootstrap for two samples and complicated data structures 7. Bootstrap-based inference
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Bradley Efron, Robert Tibshirani. An Introduction to Bootstrap. Chapman & Hall. 1998
  • Sheldon Ross. Simulation. Academic Press. 2013
Additional Bibliography
  • Ralf Korn, Elke Korn, Gerald Kroisandt. Monte Carlo Methods and Models in Finance and Insurance. Chapmann & Hall/CRC. 2010

The course syllabus may change due academic events or other reasons.