First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
STATISTICAL MODELS
SCP4063245, A.A. 2018/19

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

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
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL MODELS
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-laurea-magistrale
Department of reference Department of Statistical Sciences
Mandatory attendance No
Language of instruction English
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 Teacher in charge not defined yet.
Other lecturers LUISA BISAGLIA SECS-S/03

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 6.0
Educational activities in elective or integrative disciplines SECS-S/03 Statistics for Economics 3.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 64 161.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
4 Commissione a.a.2018/19 01/10/2018 30/09/2019 BISAGLIA LUISA (Presidente)
CHIOGNA MONICA (Membro Effettivo)
GAETAN CARLO (Membro Effettivo)
TORELLI NICOLA (Membro Effettivo)

Syllabus
Prerequisites: First year Unipd Master of Statistics courses,
especially Calcolo delle probabilità, Statistica
progredito
Target skills and knowledge: The objective of the whole course is to get students acquainted with the fundamentals, basic properties and use of the most important recent modeling
techniques, to gain experience in model building and to get some hands-on experience by analysing some real data by using R, Bugs and other up-to-date
statistical software.
Examination methods: Written and oral exams
Assessment criteria: At the end of the course, students will receive only a final mark based on all 3 exams results.
Course unit contents: Generalized linear mixed models
o Introduction to the course: basic ideas
o Generalized linear models: structure and inference
o Extending GLMs: First instances of models for hierarchical data
o Generalized linear mixed models
o Introduction to hierarchical models and to GLMMs
o Likelihood inference in GLMMs
o Bayesian Hierarchical Models
o Practical sessions with R and R-Bugs

Time series analysis
o Introduction. Linear time series models.
o Linear time series models: model specification.
o Linear time series models: parameter estimation and forecasting.
o Introduction to spectral analysis
o Nonlinear models: an introduction
o Nonlinear models: Markov-Switching Models and Threshold Autoregression Models
o Long-memory models. Integer AutoRegressive models

Spatial statistics
1. Introduction to spatial statistics:
2. Estimation and modeling of spatial correlations:
3. Prediction and Interpolation (kriging):
4. Spatio-temporal modeling:
5. Second order spatial models for network data:
6. Gibbs-Markov random fields on networks:
7. Simulation and estimation of a Markov random field on a network:
8. Hierarchical spatial models and Bayesian statistics:
Planned learning activities and teaching methods: Lectures and Laboratories
Additional notes about suggested reading: Mc Cullagh, P & Nelder J.A., Generalized Linear Models. New York: Chapman & Hall, 1989.
Gelman, A. & Hill J., Data Analysis Using Regression and Multilevel/Hierarchical Models. --: Cambridge University Press, 2007.
Fahrmeir L., Tutz, G., Multivariate Statistical Modelling Based on Generalized Linear Models. --: Springe, 2001. chapter 6
McCulloch, C.E., Searle, S.R., Generalized, Linear and Mixed Models. --: Wiley, 2001.
Brockwell P.J., Davis R.A., Introduction to Time Series and Forecasting. --: Springer, 1996.
Fan J., Yao Q., Nonlinear time series. --: Springer-Verlag, 2003.
Tsay R.S., Analysis of Financial Time Series. - -: Wiley-Interscience, 2005.
Wei W., Time Series
Banerjee, S. ,Carlin, B.P. and Gelfand. A.E (2014) Hierarchical Modeling and Analysis for Spatial Data, CRC Press, New York (second edition)
Gaetan, C. and Guyon, X. (2010) Spatial Statistics and Modeling, Springer, New York.
Textbooks (and optional supplementary readings)