Functional data analysis (FDA) is a statistical framework that allows for the analysis of
curves, images, or functions on higher dimensional domains. The goals of FDA, such as …

The mean function is a central object of inquiry in the analysis of functional data. Typical
questions related to the mean function include quantifying estimation uncertainty, testing …

Many theoretical results for lasso require the samples to be iid Recent work has provided
guarantees for lasso assuming that the time series is generated by a sparse Vector …

Functional data analysis is typically conducted within the L ²-Hilbert space framework. There
is by now a fully developed statistical toolbox allowing for the principled application of the …

Motivated by the pressing request of methods able to create prediction sets in a general
regression framework for a multivariate functional response, we propose a set of conformal …

Functional data analysis provides a popular toolbox of functional models for the analysis of
samples of random functions that are real valued. In recent years, samples of time-varying …

A new functional ANOVA test, with a graphical interpretation of the result, is presented. The
test is an extension of the global envelope test introduced by Myllymaki et al.(2017, Global …

Motivated by recent work involving the analysis of biomedical imaging data, we present a
novel procedure for constructing simultaneous confidence corridors for the mean of imaging …

Quantifying uncertainty using confidence regions is a central goal of statistical inference.
Despite this, methodologies for confidence bands in functional data analysis are still …

We investigate statistical inference for the mean function of stationary functional time series
data with an infinite moving average structure. We propose a B-spline estimation for the …