SPATIAL STATISTICS AND STATISTICAL TOOLS FOR ENVIRONMENTAL DATA

Obiettivi formativi

Learning goals The student at the end of the course should be able to use with knowledge advanced modeling and exploratory techniques specifically developed for spatially dependent data. This is achieved by assigning several homeworks on real data. Practical sessions with the R software are part of each lecture, so to allow students to implement what is taught in the theoretical part. Among the expected results, ability to elaborate environmental data using R software, ability to interpret the results obtained, ability to choose the most suitable statistical models according to the hypotheses they are founded on and to their compatibility with the data available. Knowledge and understanding The student will be able to understand the main tools for the analysis of spatial and spatio-temporal data. Also an introductory knowledge of extreme value estimation and modeling will be part of his cultural heritage Applying knowledge and understanding Students will be involved in the discussion and analysis of case studies using the open source statistical software R. Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed. Making judgements Through the homeworks and the final presentations discussions, tudente will develop judgements capacity in terms of theoretical choices in representation of real worls phenomena. Communication skills Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed. This procedure will help the student to develop his/her ability to communicate the results of its work. Learning skills One of the aims of the course is to build a statistical glossary and a dictionary of specific statistical concepts that will allow the student to read and understand scientific papers using advanced statistical tools in the analysis of environmental data.

Canale 1
GIOVANNA JONA LASINIO Scheda docente

Programmi - Frequenza - Esami

Programma
Gli argomenti del corso sono i seguenti e sono affrontati in lingua inglese: Introduction to Spatial Statistic - Exploratory Data Analysis using R - Non stochastic approaches to spatial interpolation Geostatistical Models - Spatial Interpolation under a stochastic model: mixed effect spatial model - Estimation in the likelihood paradigm - Bayesian Estimation (with a brief introduction to general Bayesian statistics) Computational issues in spatial statistics - The big N problem and some way to deal with it Lattice Models - Spatial glm and automodels Extreme Value theory - Generalized extreme value distribution - Genralized Pareto distribution Spatio temporal modeling of environmental data: some examples The evaluation of the acquired skills is done through a series of homeworks that will be assigned during classes and posted on the course web page so that they can be developed by not attending students, and a final report based on the homeworks results.
Prerequisiti
The students are expected to have a good knowledge of statistical inference. It is suggested to acquire a good knowledge of linear and nonlinear models (generalized additive models) and some basic knowledge on mixed-effects models. It is also recommended to be familiar with the software environment R.
Testi di riferimento
Slides of the lectures and other teaching material are available online in the DSS elearning web page. Suggested text books: - Handbook of Spatial Statistics Editors Alan E. Gelfand, Peter Diggle, Peter Guttorp, Montserrat Fuentes. CRCpress - Stuart Coles Introduction to Extreme Value theory Springer A more advanced text books is Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand Hierarchical Modeling and Analysis for Spatial Data, Second Edition CRCpress
Modalità insegnamento
la modalità di svolgimento non è di scelta del docente ma dipende dalle direttive di Sapienza e del governo. Ogni classe prevede una parte teorica ed una applicativa su dati reali. - Inizio corso prima settimana Introduction to Spatial Statistic - Exploratory Data Analysis using R - Non stochastic approaches to spatial interpolation mese di ottobre Geostatistical Models - Spatial Interpolation under a stochastic model: mixed effect spatial model - Estimation in the likelihood paradigm - Bayesian Estimation (with a brief introduction to general Bayesian statistics) Computational issues in spatial statistics - The big N problem and some way to deal with it Mese di novembre Lattice Models - Spatial glm and automodels Extreme Value theory - Generalized extreme value distribution - Genralized Pareto distribution mese di dicembre Spatio temporal modeling of environmental data: some examples
Frequenza
Gli studenti possono seguire o meno come da loro disponibilità, la frequenza è suggerita perché si svolgono molte attività di uso generale in classe. Sulla bacheca del docente sono disponibili tutte le indicazioni.
Modalità di esame
Alla fine del corso si determinerà il voto sulla base degli homework consegnati e di un report finale (individuale) in inglese che riassume le attività svolte negli homewrok.
Bibliografia
- Handbook of Spatial Statistics Editors Alan E. Gelfand, Peter Diggle, Peter Guttorp, Montserrat Fuentes. CRCpress - Stuart Coles Introduction to Extreme Value theory Springer - Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand Hierarchical Modeling and Analysis for Spatial Data, Second Edition CRCpress
Modalità di erogazione
la modalità di svolgimento non è di scelta del docente ma dipende dalle direttive di Sapienza e del governo. Ogni classe prevede una parte teorica ed una applicativa su dati reali. - Inizio corso prima settimana Introduction to Spatial Statistic - Exploratory Data Analysis using R - Non stochastic approaches to spatial interpolation mese di ottobre Geostatistical Models - Spatial Interpolation under a stochastic model: mixed effect spatial model - Estimation in the likelihood paradigm - Bayesian Estimation (with a brief introduction to general Bayesian statistics) Computational issues in spatial statistics - The big N problem and some way to deal with it Mese di novembre Lattice Models - Spatial glm and automodels Extreme Value theory - Generalized extreme value distribution - Genralized Pareto distribution mese di dicembre Spatio temporal modeling of environmental data: some examples
  • Codice insegnamento1047802
  • Anno accademico2024/2025
  • CorsoStatistical Methods and Applications - Metodi statistici e applicazioni
  • CurriculumData analyst (percorso valido anche ai fini del conseguimento del doppio titolo italo-francese)
  • Anno2º anno
  • Semestre1º semestre
  • SSDSECS-S/02
  • CFU9
  • Ambito disciplinareStatistico