Simulation and Resampling
Code: 104868 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Applied Statistics | OB | 3 |
Contact
- Name:
- Aureli Alabert Romero
- Email:
- aureli.alabert@uab.cat
Teachers
- Roger Borrās Amoraga
- (External) Aureli Alabert
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
It is assumed that the student has acquired the competences of the previous courses in Statistics Inference, Probability, and Stochastic Processes, and that they has a good level with the R programming language.
Objectives and Contextualisation
- Learn how to generate samples using a computer and apply it to the analysis of complex systems and process optimization.
- Learn resampling techniques in statistical inference and machine learning.
Learning Outcomes
- KM15 (Knowledge) Identify simulation and resampling algorithms and techniques, and models for complex situations, fostering innovation in the field of statistics.
- SM15 (Skill) Solve unconventional inference problems using simulation and resampling techniques.
Content
- Permutation tests: Two-sample tests. Test with paired data. Correlation tests. Advanced examples.
- Bootstrap and other resampling methods: Basic concepts. Estimations of standard error and bias. Parametric bootstrap. Non-parametric bootstrap. Mehtods to compute confidence intervals. Applications (linear and generalised-linear models, hyothesis testing, time series, ...).
- Resampling for machine learning: Bagging. Boosting.
- Simulation: Simulation of random variables and vectors. Discrete Event Simulations. Output analysis. Imput modelling. Generation of random numbers.
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Classroom lectures (theoretical and practical) | 50 | 2 | KM15, SM15, KM15 |
| Type: Autonomous | |||
| Assignments | 48 | 1.92 | |
| Personal study of the subject | 48 | 1.92 |
The metodology will combine classroom lectures delivered by the teachers and practical work of the student with computers.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Exam of Resampling | 40% | 2 | 0.08 | KM15, SM15 |
| Exam of Simulation | 405 | 2 | 0.08 | KM15, SM15 |
| Resampling assignments hand in | 10% | 0 | 0 | KM15, SM15 |
| Simulation Assignments hand in | 10% | 0 | 0 | KM15, SM15 |
Assessment Criteria
- Exams: 80% of the final grade
- Assignments: 20% of the final grade
To pass the course, students must:
- Achieve an average score of 5.0 out of 10 in the exams, with no individual score below 4.0
- Obtain an overall average of 5.0 out of 10, which will represent the final course grade
Grades that do not meet these requirements may be reviewed on a case-by-case basis.
Each exam will have a resit opportunity (“recuperation” in UAB's official terminology). Attending a resit automatically annuls the original exam grade. Assignments are not eligible for resubmission. Exams from different parts of the course may be scheduled on the same day within the same resit period.
A student will be considered eligible for evaluation if they have submitted assignments or taken exams covering at least 50% of the course weight, as indicated in the Evaluation Activities table. Otherwise, their status will appear as “Not Assessable.”
Grades from the resit period will not be considered for the possible awarding of Honors Distinction ("Matrícula d'Honor").
Plagiarism or copying, whether in assignments or during exams, will be treated equally and will result in an automatic fail for the course.
Single Assessment:
Students who choose the single assessment modality will be evaluated through a single comprehensive exam covering all course content, including practicals. No assignments will be submitted. Part of the exam may include an oral component. If students pass the first sitting, no resit option will be availableto improve the grade.
Bibliography
- Law (2014) Simulation. Modelling and Analysis.
- James - Witten - Hastie - Tibshirani (2013) An introduction to Statistical Learning: with applications in R. Springer (Recurs electrònic UAB).
- Efron - Hastie (2016) Computer Age Statistical Inference. Cambridge University Press.
Software
During the course the relevant installation instructions for the software to be used will be given, at the appropriate time.
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PLAB) Practical laboratories | 1 | Catalan | second semester | afternoon |
| (TE) Theory | 1 | Catalan | second semester | afternoon |