Stampa la pagina Condividi su Google Condividi su Twitter Condividi su Facebook Statistical Modeling

Professor Alfonso Iodice D'Enza


Prerequisites: None


Simple linear regression: estimating coefficients; assessing the accuracy of the estimates and of the model. Multiple linear regression. Qualitative predictors. Extensions of the linear model. Potential problems. Linear regression and K-nearest neighbors regression. Non-linear models for regression. Polynomial regression; step functions; basis functions, regression splines. Smoothing splines. Local regression. Generalized additive models.


An Introduction to Statistical Learning, with application in R. G. James, D. Witten, T. Hastie and R. Tibshirani. Freely downloadable here


The course aim is to provide a modern approach to statistical models, with a special focus on regression problems. Linear and non-linear models are defined in light of the bias-variance trade-off and of the flexibility-interpretability trade-off. All the methods are introduced from both a theoretical and applicative perspective, and compared to one another. Model selection and model performance assessment will be also addressed. All the covered topics will be implemented in Cran-R meta-language.


Lectures and lab sessions.

Examination methods:

Final project dissertation.


[Ultima modifica: mercoledì 30 novembre 2016]