Presentazione

Organizzazione della Didattica

DM270
SCIENZE STATISTICHE ORD. 2014


9

Corsi comuni

 

Frontali Esercizi Laboratorio Studio Individuale
ORE: 64 0 0 69

Periodo

AnnoPeriodo
II anno2 semestre

Frequenza

Facoltativa

Erogazione

Convenzionale

Lingua

Inglese

Calendario Attività Didattiche

InizioFine
26/02/201801/06/2018

Tipologia

TipologiaAmbitoSSDCFU
affine/integrativo Nessun ambitoSECS-S/016
affine/integrativo Nessun ambitoSECS-S/033


Responsabile Insegnamento

ResponsabileSSDStruttura
Prof.ssa BISAGLIA LUISASECS-S/03Dipartimento di Scienze Statistiche

Altri Docenti

DocenteCoperturaSSDStruttura
DA ASSEGNARE-N.D.
DA ASSEGNARE-N.D.

Attività di Supporto alla Didattica

Non previste.

Bollettino

Calcolo delle probabilità, Statistica progredito

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.

Lectures and Laboratories

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:

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.

At the end of the course, students will receive only a final mark based on all 3 exams results.

CONTENUTO NON PRESENTE

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.