activity
20202026
most citedSpatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation

13 citations · 37 across the 12 of their papers we have counts for

collaborators

12 papers

stat.ME2026

Testing the Structural Properties of Marked Point Processes Using Local Inhomogeneous Mark-Weighted K-Functions

Nicoletta D'Angelo, Giada Adelfio, Matthias Eckardt

This work proposes -type test statistics to assess different hypotheses on the local structure of an observed marked point pattern. The test statistics is based on the local i…

astro-ph.SR2025

Using a neural network approach and starspots dependent models to predict effective temperatures and ages of young stars

Marco Tarantino, Loredana Prisinzano, Nicoletta D Angelo +2

This study presents a statistical approach to accurately predict the effective temperatures of pre-main sequence stars, which are necessary for determining stellar ages using the i…

stat.ME2024

stopp: An R Package for Spatio-Temporal Point Pattern Analysis

Nicoletta D'Angelo, Giada Adelfio

stopp is a novel R package specifically designed for the analysis of spatio-temporal point patterns which might have occurred in a subset of the Euclidean space or on some specific…

stat.ME2024

Semi-parametric profile pseudolikelihood via local summary statistics for spatial point pattern intensity estimation

Nicoletta D'Angelo, Giada Adelfio, Jorge Mateu +1

Second-order statistics play a crucial role in analysing point processes. Previous research has specifically explored locally weighted second-order statistics for point processes,…

stat.ME2024

Cubature scheme for spatio-temporal Poisson point processes estimation

Nicoletta D'Angelo, Giada Adelfio

This work presents the cubature scheme for the fitting of spatio-temporal Poisson point processes. The methodology is implemented in the R Core Team (2024) package stopp (D'Angelo…

stat.AP2024★ 1 cited

Spatio-temporal point process modelling of fires in Sicily exploring human and environmental factors

Nicoletta D'Angelo, Alessandro Albano, Andrea Gilardi +1

In 2023, Sicily faced an escalating issue of uncontrolled fires, necessitating a thorough investigation into their spatio-temporal dynamics. Our study addresses this concern throug…

stat.ME2023

Minimum contrast for the first-order intensity estimation of spatial and spatio-temporal point processes

Nicoletta D'Angelo, Giada Adelfio

In this paper, we harness a result in point process theory, specifically the expectation of the weighted -function, where the weighting is done by the true first-order intensity…

stat.ME2023

stopp: Methods for spatio-temporal point pattern analysis, simulation, model fitting, diagnostics, and local analyses

Nicoletta D'Angelo, Giada Adelfio

The stopp R package deals with spatio-temporal point processes which might have occurred on the Euclidean space or on some specific linear networks such as roads of a city. The pac…

stat.ME2022★ 12 cited

Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes

Nicoletta D'Angelo, Giada Adelfio, Jorge Mateu

We propose a local version of spatio-temporal log-Gaussian Cox processes using Local Indicators of Spatio-Temporal Association (LISTA) functions into the minimum contrast procedure…

stat.ME2022

Feature detection in point processes on linear networks using nearest neighbour volumes

Juan F. Diaz-Sepulveda, Nicoletta D'Angelo, Giada Adelfio +2

We consider the feature detection problem in the presence of clutter in point processes on linear networks. We extend the classification method developed in previous studies to thi…

stat.ME2022★ 11 cited

Local inhomogeneous weighted summary statistics for marked point processes

Nicoletta D'Angelo, Giada Adelfio, Jorge Mateu +1

We introduce a family of local inhomogeneous mark-weighted summary statistics, of order two and higher, for general marked point processes. Depending on how the involved weight fun…

stat.AP2020★ 13 cited

Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation

Nicoletta D'Angelo, Antonino Abbruzzo, Giada Adelfio

We consider latent Gaussian fields for modelling spatial dependence in the context of both spatial point patterns and areal data, providing two different applications. The inhomoge…