| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Science | ||
| STATISTICS FOR TECHNOLOGY AND SCIENCE | ||
Course unit
COMPUTATIONAL STATISTICS
SCP4063598, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
COMPUTATIONAL STATISTICS
SCP4063598, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
Information on the course unit
| Degree course |
First cycle degree in STATISTICS FOR TECHNOLOGY AND SCIENCE SC2094, Degree course structure A.Y. 2014/15, A.Y. 2019/20 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | 9.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | COMPUTATIONAL STATISTICS | |
| Website of the academic structure | http://www.stat.unipd.it/studiare/ammissione-lauree-triennali | |
| Department of reference | Department of Statistical Sciences | |
| E-Learning website | https://elearning.unipd.it/stat/course/view.php?idnumber=2019-SC2094-000ZZ-2018-SCP4063598-N0 | |
| Mandatory attendance | No | |
| Language of instruction | Italian | |
| Branch | PADOVA | |
| Single Course unit | The Course unit can be attended under the option Single Course unit attendance | |
| Optional Course unit | The Course unit can be chosen as Optional Course unit |
Lecturers
| Teacher in charge | MATTEO GRIGOLETTO | SECS-S/01 |
|---|
Mutuated
| Course unit code | Course unit name | Teacher in charge | Degree course code |
|---|---|---|---|
| SCP4063598 | COMPUTATIONAL STATISTICS | MATTEO GRIGOLETTO | SC2095 |
ECTS: details
| Type | Scientific-Disciplinary Sector | Credits allocated | |
|---|---|---|---|
| Core courses | SECS-S/01 | Statistics | 9.0 |
Course unit organization
| Period | Second semester |
|---|---|
| Year | 2nd Year |
| Teaching method | frontal |
| Type of hours | Credits | Teaching hours |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| Laboratory | 3.0 | 22 | 53.0 | No turn |
| Lecture | 6.0 | 42 | 108.0 | No turn |
| Start of activities | 02/03/2020 |
|---|---|
| End of activities | 12/06/2020 |
| Show course schedule | 2019/20 Reg.2014 course timetable |
Examination board
| Board | From | To | Members of the board |
|---|---|---|---|
| 5 Commissione a.a.2019/20 | 01/10/2019 | 30/09/2020 |
GRIGOLETTO
MATTEO
(Presidente)
CANALE ANTONIO (Membro Effettivo) SCARPA BRUNO (Membro Effettivo) |
| Prerequisites: | The following previous courses are required: Mathematics, Statistics I and II, Linear algebra, Probability. |
| Target skills and knowledge: | Knowledge of computational tools useful for inferential purposes. Programming abilities that allow the implementation, with the software R, of functions that apply the required algorithms. |
| Examination methods: | Written exam in which the student is required to write and comment programs in R, with the objective to solve specific inferential problems. |
| Assessment criteria: | Evaluation of the understanding of theoretical and practical computational tools useful for solving inferential problems. |
| Course unit contents: | Simulation techniques and statistical applications.
Introduction to simulation: generation from uniform random variables, generation by inversion, generation by acceptance-rejection, importance sampling, Rao-Blackwell, antithetic variables. Applications: multidimensional integrals, evaluation of efficiency and robustness of inferential methods, hypotheses testing in non-standard settings. Inference with Bootstrap. Introduction to Bootstrap, parametric and nonparametric Bootstrap, application examples (quantiles, linear models). Nonparametric estimation. Density function: the kernel method, the choice of the smoothing parameter, automatic criteria (cross validation, Sheather-Jones). Regression function: local polynomial regression, splines, equivalent degrees of freedom, AICc and GCV, using the Bootstrap for evaluating precision. Applications to real data. Numerical exploration of the likelihood function. Introduction to numerical optimization and differentiation algorithms in R. Use of these algorithms for computing maximum likelihood estimators. Confidence regions based on the profile likelihood or on the Fisher information matrix. |
| Planned learning activities and teaching methods: | Lectures and laboratories, all based on the software R. Teaching is always interactive, with questions and presentation of case studies that provoke critical discussion. |
| Additional notes about suggested reading: | The lectures and laboratories are all available in the Moodle platform. In the same platform past exams, data sets and more teaching materials are also available. |
| Textbooks (and optional supplementary readings) |
|
- Lecturing
- Laboratory
- Problem based learning
- Case study
- Loading of files and pages (web pages, Moodle, ...)