Estimating the conditional distribution in functional regression problems

Description

We consider the problem of consistently estimating the conditional distribution of a functional data object Y given covariates X in a general space, assuming that the response Y and the predictor X are related by a functional regression model.
Two estimation methods are proposed, based on either the empirical distribution of the estimated model residuals, or fitting functional parametric models to the model residuals.
In the case of functional linear regression, consistent estimation of the conditional distribution can be achieved, both when Y is an element of a separable Hilbert space, and when Y is an element of the Banach space of continuous functions. This means that sets A specifying interesting path properties of Y can be considered.

Period	16 Oct 2021
Event title	25th Young Statisticians Meeting in Vorau
Event type	Conference