First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP4063598, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
SC2095, Degree course structure A.Y. 2014/15, A.Y. 2018/19
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
Department of reference Department of Statistical Sciences
E-Learning website
Mandatory attendance No
Language of instruction Italian
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

Teacher in charge MATTEO GRIGOLETTO SECS-S/01

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 9.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 3.0 22 53.0 No turn
Lecture 6.0 42 108.0 No turn

Start of activities 25/02/2019
End of activities 14/06/2019
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)
4 Commissione a.a.2018/19 01/10/2018 30/09/2019 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)

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Interactive lecturing
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • R