Nonparametric regression models offer a way to understand and quantify relationships
between variables without having to identify an appropriate family of possible regression …

We introduce fast multilevel functional principal component analysis (fast MFPCA), which
scales up to high dimensional functional data measured at multiple visits. The new approach …

Semiparametric regression models have received considerable attention over the last
decades, because of their flexibility and their good finite sample performances. Here we …

The vast amount of data generated nowadays demands innovative and flexible techniques
that allow to accommodate expert knowledge and help in decision-making. In this work, we …

Massive data bring the big challenges of memory and computation for analysis. These
challenges can be tackled by taking subsamples from the full data as a surrogate. For …

We address the problem of performing inference on the linear and nonlinear terms of a
semiparametric spatial regression model with differential regularisation. For the linear term …

Local polynomial fitting exhibits numerous compelling statistical properties, particularly
within the intricate realm of multivariate analysis. However, as functional data analysis gains …

Asymptotic properties of penalized splines for functional data Page 1 Bernoulli 26(4), 2020,
2847–2875 https://doi.org/10.3150/20-BEJ1209 Asymptotic properties of penalized splines for …

Motivated by map** adverse artifactual events caused by body movements in
electroencephalographic (EEG) signals, we present a functional independent component …

This paper develops a general theory on rates of convergence of penalized spline
estimators for function estimation when the likelihood functional is concave in candidate …