First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
STATISTICS (ADVANCED)
SCP4063084, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
STATISTICAL SCIENCES
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2018/19
N0
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICS (ADVANCED)
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-laurea-magistrale
Department of reference Department of Statistical Sciences
E-Learning website https://elearning.unipd.it/stat/course/view.php?idnumber=2018-SS1736-000ZZ-2018-SCP4063084-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 ANTONIO CANALE SECS-S/01
Other lecturers EULOGE CLOVIS KENNE PAGUI SECS-S/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/01 Statistics 9.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Practice 2.0 26 24.0 No turn
Lecture 7.0 56 119.0 No turn

Calendar
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.2018/19 01/10/2018 30/09/2019 CANALE ANTONIO (Presidente)
ADIMARI GIANFRANCO (Membro Effettivo)
DI CATERINA CLAUDIA (Membro Effettivo)
KENNE PAGUI EULOGE CLOVIS (Membro Effettivo)
SALVAN ALESSANDRA (Membro Effettivo)

Syllabus
Prerequisites: Probability Theory (Calcolo delle Probabilità). Strong background in Calculus and Linear Algebra.
Target skills and knowledge: Knowledge: essential statistical toolbox for the understanding and solving of problems in methodological Statistics.
Skills: ability to recognize the statistical toolbox used in different statistical contexts; ability to solve simple methodological problems and to understand or formulate suitable models for their description.
Examination methods: Written closed book exam. Allowed: pen (blue/black), calculator, personal notes (front side of 1 A4 sheet).
3 exam questions, each formed of 4-6 exercises and of similar difficulty.
Assessment criteria: 3 exam questions, each formed of 4-6 exercises and of similar difficulty.
Course unit contents: - Elements of statistical inference (review): point estimation, confidence intervals, hypotheses testing.
- The likelihood function: definition, examples and properties (invariance, Wald inequality). Likelihood quantities: definition (score function, observed and expected information) and properties.
- Exponential families.
- Sufficient statistics.
- Maximum likelihood estimation: definition, examples and properties (equivariance, consistency, asymptotic normality).
- Cramer Rao inequality. Uniformly minimum variance unbiased (UMVU) estimators.
- Likelihood ratio tests: definition and examples; asymptotic distribution and asymptotic equivalent tests; confidence regions based on the likelihood ratio.
- Profile likelihood.
- Neyman-Pearson lemma. Uniformly most powerful (UMP) tests.
- Pivotal quantities and estimating equations.
- Consequences of model misspecification and robust statistics.
- (Parametric) Bayesian inference: Bayes paradigm for inference; prior distributions and conjugate families; special cases (normal-normal, beta-binomial, pareto-uniform); credibility intervals and hypotheses testing.
Planned learning activities and teaching methods: Upfront classes: theory (75%), exercises (25%).
Support by: Sevizio tutorato.
Additional notes about suggested reading: Suggested reading list:
- Andreatta, G. e Runggaldier, W.J. (1983). Statistica matematica: problemi ed esercizi risolti. Liguori Editore, Napoli.
- Beaumont, G.P. (1980). Intermediate Mathematical Statistics. Chapman & Hall, London.
- Cifarelli, D.M. e Muliere, P. (1989). Statistica bayesiana. Appunti ad uso degli studenti. Gianni Iuculano Editore, Pavia.
- Welsh, A.H. (1996). Aspects of Statistical Inference. Wiley, New York.
- Peter Hoff, (2009). A First Course in Bayesian Statistical Methods, Springer,
Textbooks (and optional supplementary readings)
  • Adelchi Azzalini, Inferenza statistica: una presentazione basata sul concetto di v. MIlano: Springer-Verlag Italia, 2001. Cerca nel catalogo
  • Luigi Pace & Alessandra Salvan, Introduzione alla statistica - II - Inferenza, verosimiglianza, modelli. Padova: Cedam, 2001. Cerca nel catalogo