This work gives an overview of an innovative class of methods for the analysis of spatial and
of functional data observed over complicated two‐dimensional domains. This class is based …

In this work, we consider space-time point processes and study their continuous space-time
evolution. We propose an innovative nonparametric methodology to estimate the unknown …

An innovative nonparametric method for density estimation over general two-dimensional
Riemannian manifolds is proposed. The method follows a functional data analysis …

This paper presents a novel approach for pointwise estimation of multivariate density
functions on known domains of arbitrary dimensions using nonparametric local polynomial …

We propose a novel procedure for estimating the mean function of longitudinal imaging data
with inherent spatial and temporal correlation. We depict the dependence between …

Many spatially dependent phenomena that are of interest in environmental problems are
characterized by strong anisotropy and non‐stationarity. Moreover, the data are often …

We propose a new approach to non-parametric density estimation, that is based on
regularizing a Sobolev norm of the density. This method is provably different from Kernel …

Accurately estimating data density is crucial for making informed decisions and modeling in
various fields. This paper presents a novel nonparametric density estimation procedure that …

The projection of sample measurements onto a reconstruction space represented by a basis
on a regular grid is a powerful and simple approach to estimate a probability density …

Based on the investigation of logistic regression models utilizing sparse triangulation within
a compact domain in ℝ 2, this study addresses the limited research extending the triogram …