First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
STATISTICAL MODELS
SCP4063245, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course Second cycle degree in
STATISTICAL SCIENCES
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2017/18
N0
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL MODELS
Website of the academic structure http://scienzestatistiche.scienze.unipd.it/2017/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 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

Mode of delivery (when and how)
Period Second semester
Year 2nd Year
Teaching method frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours of
Individual study
Shifts
Lecture 9.0 64 161.0 No turn

Calendar
Start of activities 26/02/2018
End of activities 01/06/2018

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: A written exam for each parts of the course.
Each exam will be marked independently by the corresponding instructor.
At the end of the course, students will receive a final mark based on all 3 exams results.
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)