Many scientific and economic applications involve the statistical learning of high-
dimensional functional time series, where the number of functional variables is comparable …

This paper deals with the problem of the nonparametric estimation of the functional expectile
regression. We use the local linear approach to construct a new estimator of the studied …

Nonparametric regression on Lie groups with measurement errors Page 1 The Annals of
Statistics 2022, Vol. 50, No. 5, 2973–3008 https://doi.org/10.1214/22-AOS2218 © Institute of …

Deep operator learning has emerged as a promising tool for reduced-order modelling and
PDE model discovery. Leveraging the expressive power of deep neural networks, especially …

This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric
model designed for analysis of high-dimensional functional time series. DF2M is built upon …

This paper extends the linearized maximum rank correlation (LMRC) estimation proposed
by Shen et al.(2023) to the setting where the covariate is a function. However, this extension …

We are concerned with the nonparametric estimation of the expectile functional regression.
More precisely, we build an estimator, by the local linear smoothing approach, of the …

Recently, two-way or longitudinal functional data analysis has attracted much attention in
many fields. However, little is known on how to appropriately characterize the association …

A novel method for noise reduction in the setting of curve time series with error
contamination is proposed, based on extending the framework of functional principal …

As one of the most powerful tools for examining the association between functional
covariates and a response, the functional regression model has been widely adopted in …