Advanced statistical models - Generalized linear models

Saverio Ranciati

  • Date:

    09 FEBRUARY
    -
    20 FEBRUARY 2026
     from 14:00 to 17:00

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

  • 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.
Module 1. Generalized Linear Models and extensions
  • Exponential Families and GLM definition
  • Estimation/fit and inference
  • GLMs for non-negative response variable
  • GLMs for count data (extension: overdispersed and zero-inflated data)
  • GLMs for binary and multinomial data (extension: imbalanced classes)
The exam consists in a presentation based on a paper that will be assigned by the lecturer.