Checking date: 11/08/2023

Course: 2023/2024

Regression Models
(17758)
Master in Statistics for Data Science (Plan: 386 - Estudio: 345)
EPI

Coordinating teacher: DURBAN REGUERA, MARIA LUZ

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:

Requirements (Subjects that are assumed to be known)
Probability Statistical Inference Programming in R
Objectives
The main course objectives are: 1. Understand and apply linear regression for estimation, inference, and diagnostics. 2. Introduce the concept of Generalized Linear Models (GLMs) with a focus on the Exponential family, and develop skills in estimation, inference, and diagnostics for GLMs. 3. Explore logistic regression, along with Multinomial, Ordinal, and Poisson models. 4. Gain knowledge of Generalized Additive Models (GAMs) and become proficient in smoothing methods, penalized splines, estimation, and the selection of smoothing parameters.
Skills and learning outcomes
Description of contents: programme
Regression Models 1) Linear regression: Estimation. Inference. Diagnostics. 2) Introduction to Generalized Linear Models: Exponetial family. Estimation. Inference. Diagnostics. 4) Logistic Regression, Multinomial, Ordinal, Poisson. 3) Generalized Additive Models: Smoothing Methods. Penalized Splines. Estimation. Smoothing parameter selection
Learning activities and methodology
Learning activities: Master classes Exercises Computer labs Projects Teaching methodologies: Presentations of the professor in class with computing and visual media, where the professor develops the mail concepts of the subject and provides bibliography supplementing the knowledge of students. Critical reading of texts recommended by the professor: manuals and/or academic papers, either for their posterior discussion in class, or for widening and consolidating the subject matter.
Assessment System
• % end-of-term-examination 50
• % of continuous assessment (assigments, laboratory, practicals...) 50

Calendar of Continuous assessment

Basic Bibliography
• Annette J. Dobson, Adrian G. Barnett . An Introduction to Generalized Linear Models. CRC Press. 2018
• Julian J. Faraway. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC Press. 2016
• Michael H. Kutner, Chris J. Nachtsheim, John Neter. Applied Linear Regression Models. McGraw-Hill Higher Education. 2003
• P. McCullagh, John A. Nelder. Generalized Linear Models. CRC Press. 1989
• Simon Wood. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC Texts in Statistical Science. 2017

The course syllabus may change due academic events or other reasons.