Functional data analysis (FDA) is a fast-growing area of research and development in
statistics. While most FDA literature imposes the classical L 2 Hilbert structure on function …

In the present paper, we develop strong uniform consistency results for the generic kernel
(including the kernel density estimator) on Riemannian manifolds with Riemann integrable …

statistics represent a fundamental class of statistics from modeling quantities of interest
defined by multi-subject responses.-statistics generalize the empirical mean of a random …

We consider estimation of mean and covariance functions of functional snippets, which are
short segments of functions possibly observed irregularly on an individual specific …

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 …

Riemannian optimization is a principled framework for solving optimization problems where
the desired optimum is constrained to a smooth manifold $\mathcal {M} $. Algorithms …

This paper studies the problem of functional data clustering. Functional data have their own
characteristics and contain rich information that cannot be obtained when regarding the data …

The one-sample test and two-sample test for the mean of high-dimensional functional time
series are considered in this study. The proposed tests are built on the dimension-wise max …

Functional data often arise in the areas where the causal treatment effect is of interest.
However, research concerning the effect of a functional variable on an outcome is typically …

In the (special) smoothing spline problem one considers a variational problem with a
quadratic data fidelity penalty and Laplacian regularisation. Higher order regularity can be …