Model fitting and prediction in the presence of correlation due to temporal and/or spatial association. ARIMA models and Gaussian processes.
By the end of the course, students are expected to be able to:
- Explain, with examples, the main inferential tasks related to spatial or temporal data, and how the spatial or temporal associations make it possible to borrow statistical tools.
- Understand the ideas of autocorrelation and correlated errors, and be able to explain the importance of these ideas for temporal and spatial modelling.
- Fit temporal, spatial, and spatio-temporal models by implementing them in a probabilistic programming language, and interpret the results.
- Apply relevant visualization tools and draw correct conclusions from the analysis.
- Shaddick, Gavin and Zidek, James V. Spatio-Temporal Methods in Environmental Epidemiology. CRC Press, 2016.
- Chatfield, Chris. The Analysis of Time Series: An Introduction. CRC Press, 2003.
Note: information on this page is preliminary and subject to change.