Advanced statistical models - Spatial statistics

Anna Gloria Billè; Silvia Emili;

  • Date:

    08 APRIL
    -
    22 APRIL 2026
     from 15:00 to 18:00

  • Event location: Aula IV, Department of Statistical Sciences, Via Belle Arti 41 (STAT PhD Classes Virtual Room on need) - In presence and online event

  • Type: Cycle 41 - Mandatory Courses

Aims: To introduce advanced modelling strategies for independent data or data characterized by spatial or temporal dependence including model selection, inference, prediction.
Learning outcomes: At the end of the course the students will have an appreciation of the different modelling strategies for data characterized by various kinds of dependencies and will be able to deal with estimation issues, to perform model selection and to apply the models to real data situations.

Course content

Module 4. Specification and Inference for models with cross-sectional (spatial) dependence

  • Fundamental concepts of Bayesian hierarchical modeling with an introduction to Latent Gaussian Models
  • Incorporating correlation structures into models using Gaussian Markov Random Fields (GMRF) and intrinsic GMRF
  • Focus on spatio-temporal models: Random Walk processes, Intrinsic Conditional Auto-Regressive (ICAR) priors, and their interactions
  • Introduction to Small Area Estimation models