- data e orario: 23 maggio 2024 ore 15.00
- aula 204, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale.
- on-line: meet.google.com/iap-gjcr-bkb

Abstract: 

Regression analysis offers tools for explaining the relation between (a set of) dependent and independent variables. Recent advances extend the available response types from uni- or multivariate to functional, and more recently also distributional responses. For functional distributions, often represented through probability density functions, the Bayes space framework was developed to respect the relative information they carry. The Hilbert space structure of Bayes space enables one to utilize standard methods of functional data analysis for the use on - properly transformed - density functions. Focusing on regression analysis, it is possible to construct adequate models with densities on the side of both the dependent and independent variables.

In this seminar, a brief overview of regression models for univariate densities will be presented. A closer attention will be given to the possible generalization of additive regression model for bivariate distributions. Finally, an outlook at the Bayesian inference in linear regression with probability densities will be discussed. All proposed methods will be demonstrated on real data from the fields of geochemistry and demographics.