STATISTICS FOR SPATIO-TEMPORAL DATA

This is a master-level topic course on spatio-temporal modeling as part of the interdisciplinary activities of the Master's Degree Program in Computer Science ( Curriculum in Data Management and Analytics). Together with the other courses on statistical inference and learning and on statistical computation and simulation, it offers a spectrum of modern statistical techniques that nowadays represent the most required expertises. The major emphasis is on statistical models for discrete time data. Topics covered in the course will also include geostatistical models and spatial prediction. The focus is on applications with real data and their analysis with statistical programs such as R.

Produce informative visual displays of temporal and spatial data
Perform exploratory data analyses
Select appropriate statistical models for various types of temporal or spatial data
Estimate model parameters with modern statistical software ( R)
Interpret software output
Effectively communicate analysis in a written document and oral presentation

Probability and Mathematical Statistics at the level of Rice, J.A. (2007) Mathematical Statistics and Data Analysis, Duxbury Press

Time series analysis
- Filtering
- Decomposition
- Prediction
- ARMA Models

Geostatistical models
- Prediction

Space-time models

Shumway, Robert H., Stoffer, David S. (2017) Time Series Analysis and Its Applications: With R Examples. Springer
Cressie, N.A.C, Wikle, C.K. (2011) Statistics for Spatio-Temporal Data. Wiley, New York

Final examination will consist of two steps:
1) preparation of an individual report regarding the analysis of an dataset.
The class project will entail choosing a problem (mutually agreed upon), writing code to solve it, and write a report which provides the background, motivation, solution method used and results. This project should ideally be a task you need to do for your thesis so that it is serves multiple purposes.
2) oral illustration of the report.

Grades will be determined by an oral exam (50%) and by a project (50%),

This course is based on lectures, which will cover the major topics, emphasizing and discussing the important points.
Theoretical lectures will be complemented by exercise classes and lab sessions. The statistical software used in the course is R (www.r-project.org).
The personal participation is important, and it is will help the student to learn more efficiently to read the assigned material to reinforce the lectures.
R scripts from various sources may be used to reinforce the material.

English

oral