Formazione e ricerca

Attività Formative del Corso di Dottorato - 2023/2024

This page shows the courses and classes of the PhD programme for the academic year 2023/2024. Additional courses and classes will be added during the year. Please check for updates regularly!

Attività Formative della Scuola di Dottorato - 2023/2024

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.

Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, no confirmation e-mail will be sent after signing up.

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

Crediti

2

Lingua di erogazione

English

Frequenza alle lezioni

Scelta Libera

Sede

VERONA

Obiettivi di apprendimento

An introduction to the fundamentals of generalized regression models will be given in this course, with a focus on models for count, binary, and categorical data. These types of response variables are widely used in industrial applications as well as observational and experimental research.
Upon successful completion of the course, students will be able to:
• Describe the general structure of a GLM and similarities and differences with linear models
• Estimate and interpret a logistic regression model
• Estimate and interpret a Poisson regression model
• Know of issues and some strategies for dealing with overdispersion in some generalised linear models (GLMs)

Prerequisiti e nozioni di base

This course assumes a good understanding of probability and mid-level knowledge of linear regression theory.

Programma

The course covers methods for regression analysis of responses that do not follow the normal distribution, especially of discrete responses. We will learn to understand some of the common statistical methods for fitting regression models to such data. In particular, we will consider logistic regression, Poisson regression and log-linear models. The lecture focuses on the development, theoretical justification, and interpretation of these methods.

Quando e Dove

This course assumes a good understanding of probability and mid-level knowledge of linear regression theory.
Teaching forms mainly consist of lectures (8h) and exercises proposed by the teacher. The teaching material (slides of the theoretical lessons) is made available to the students on the e-learning web page of the course (Moodle platform). Lessons will be delivered via Zoom. Full attendance is required.
19 March 2024, 14.30-16.30
26 March 2024, 14.30-16.30
3 April 2024, 14.30-16.30
24 April 2024, 14.30-16.30
Zoom link

Modalità di verifica dell'apprendimento

There is no exam

Le/gli studentesse/studenti con disabilità o disturbi specifici di apprendimento (DSA), che intendano richiedere l'adattamento della prova d'esame, devono seguire le indicazioni riportate QUI

Valutazione

There is no exam, hence there is also no definition of the evaluation criteria.

Criteri di composizione del voto finale

There is no grade because there is no exam.

Lezioni Programmate

Quando Aula Docente Argomenti
martedì 19 marzo 2024
14:30 - 16:30
Durata: 2.00
Da definire Lucia Cazzoletti Introduction to generalised linear models. Review of the general linear model: assumptions of the linear model (independence of observations, homoskedasticity of errors, linearity of coeffiicents) , least squares estimation and maximum likelihood estimation. Main features of the genralised linear model: i) the probability distribution function of the random component of the response variable belongs to the exponential family, ii) a differentiable and monotonic link function relates the mean of the response variable to the linear predictor, a linear combination of coefficients and explanatory variables.
martedì 26 marzo 2024
14:30 - 16:30
Durata: 2.00
Da definire Lucia Cazzoletti Introduction to the theoretical basis of logistic regression model, commonly used for binary (proportion/percentage) data, as a generalised linear model. Binomial distribution for the outcome binary variable. The link between probability and logodds. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance.
mercoledì 03 aprile 2024
14:30 - 16:30
Durata: 2.00
Da definire Lucia Cazzoletti Introduction to the theoretical basis of Poisson regression model, commonly used for count data, as a generalised linear model. Poisson distribution for the outcome variable. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance. Use of the offset to take into account the different exposure of subjects. Extensions of the Poisson Regression Model: Negative binomial regression model (NBRM), Zero-inflated poisson (ZIP) model, Zero-truncated count data model.
mercoledì 24 aprile 2024
14:30 - 16:30
Durata: 2.00
Aula virtuale - Lezione online Lucia Cazzoletti Using Deviances to Compare Models for Logistic and for Poisson Regression Models. Use of the Likelihood Ratio Test to assess the presence of overdispersion. Some hints about the log-linear model in the presence of contingency tables

Docenti

A B C D F G L M P R V Z

Assfalg Michael

symbol email michael.assfalg@univr.it symbol phone-number +39 045 802 7949

Astegno Alessandra

symbol email alessandra.astegno@univr.it symbol phone-number 045802 7955

Avesani Linda

symbol email linda.avesani@univr.it symbol phone-number +39 045 802 7839

Ballottari Matteo

symbol email matteo.ballottari@univr.it symbol phone-number 045 802 7823

Bassi Roberto

symbol email roberto.bassi@univr.it symbol phone-number 045 8027916

Battista Federico

symbol email federico.battista@univr.it symbol phone-number +390458027917

Bellin Diana

symbol email diana.bellin@univr.it symbol phone-number 045 802 7090

Bolzonella David

symbol email david.bolzonella@univr.it symbol phone-number 045 802 7965

Brandi Jessica

symbol email jessica.brandi@univr.it symbol phone-number 045 8027874

Capaldi Stefano

symbol email stefano.capaldi@univr.it symbol phone-number +39 045 802 7907

Cazzaniga Stefano

symbol email stefano.cazzaniga@univr.it symbol phone-number +39 045 8027075

Cecconi Daniela

symbol email daniela.cecconi@univr.it symbol phone-number +39 045 802 7056; Lab: +39 045 802 7087

Commisso Mauro

symbol email mauro.commisso@univr.it symbol phone-number 0458027030

Dainese Matteo

symbol email matteo.dainese@univr.it symbol phone-number +39 045 802 7858

Dal Corso Giovanni

symbol email giovanni.dalcorso@univr.it symbol phone-number (0039) 045 802 7867

Dall'Osto Luca

symbol email luca.dallosto@univr.it symbol phone-number +39 045 802 7806

Delledonne Massimo

symbol email massimo.delledonne@univr.it symbol phone-number 045 802 7962; Lab: 045 802 7058

Fasoli Marianna

symbol email marianna.fasoli@univr.it symbol phone-number +39 045 842 5625

Frison Nicola

symbol email nicola.frison@univr.it symbol phone-number 045 802 7857

Furini Antonella

symbol email antonella.furini@univr.it symbol phone-number 045 802 7950; Lab: 045 802 7043

Fusco Salvatore

symbol email salvatore.fusco@univr.it symbol phone-number Office: +39 045 802 7954 Lab: +39 045 802 7086

Giorgetti Alejandro

symbol email alejandro.giorgetti@univr.it symbol phone-number 045 802 7982

Guardavaccaro Daniele

symbol email daniele.guardavaccaro@univr.it symbol phone-number +39 045 802 7903

Guzzo Flavia

symbol email flavia.guzzo@univr.it symbol phone-number 045 802 7923

Lampis Silvia

symbol email silvia.lampis@univr.it symbol phone-number 045 802 7095

Mori Nicola

symbol email nicola.mori@univr.it symbol phone-number 045 683 5628

Pandolfini Tiziana

symbol email tiziana.pandolfini@univr.it symbol phone-number 045 802 7918

Pezzotti Mario

symbol email mario.pezzotti@univr.it symbol phone-number +39045 802 7951

Polverari Annalisa

symbol email annalisa.polverari@univr.it symbol phone-number 045 8425629

Rossato Marzia

symbol email marzia.rossato@univr.it symbol phone-number +39 045 802 7800

Vandelle Elodie Genevieve Germaine

symbol email elodiegenevieve.vandelle@univr.it symbol phone-number 0458027826

Vettori Andrea

symbol email andrea.vettori@univr.it symbol phone-number 045 802 7861/7862

Vitulo Nicola

symbol email nicola.vitulo@univr.it symbol phone-number 0458027982

Zoccatelli Gianni

symbol email gianni.zoccatelli@univr.it symbol phone-number +39 045 802 7952

Dottorandi

Dottorandi presenti nel:

Avesani Michele

symbol email michele.avesani@univr.it

Carlomagno Marco

symbol email marco.carlomagno@univr.it

Gorrieri Sara

symbol email sara.gorrieri@univr.it

Lucchini Filippo

symbol email filippo.lucchini@univr.it

Miotti Tea

symbol email tea.miotti@univr.it

Paini Matteo

symbol email matteo.paini@univr.it

Pezzuto Marco

symbol email marco.pezzuto@univr.it
Lezioni del Corso
Lezioni della Scuola di Dottorato

Loading...

Linee guida percorso formativo

Di seguito i file che contengono le Linee guida per il percorso formativo e il regolamento per l'acquisizione dei crediti formativi (CFU) per l'Anno Accademico 2023/2024.