Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data
DOI10.1007/s11749-020-00730-2zbMath1489.60083arXiv1911.13142OpenAlexW3080858309MaRDI QIDQ2666061
Mohammad Ghorbani, Ottmar Cronie, Jun Yu, Jorge Mateu
Publication date: 22 November 2021
Published in: Test (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.13142
nonparametric estimationfunctional data analysiscorrelation functionalintensity functionalspatio-temporal geostatistical markingweighted marked reduced moment measure
Directional data; spatial statistics (62H11) Inference from spatial processes (62M30) Random fields; image analysis (62M40) Functional data analysis (62R10) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Cross-covariance functions for multivariate geostatistics
- Summary statistics for inhomogeneous marked point processes
- Estimating second-order characteristics of inhomogeneous spatio-temporal point processes
- Some edge correction methods for marked spatio-temporal point process models
- The analysis of marked point patterns evolving through space and time
- A note on gaps in proofs of central limit theorems
- A second order approach to analyse spatial point patterns with functional marks
- Spatial statistics in the presence of location error with an application to remote sensing of the environment
- Resample-smoothing of Voronoi intensity estimators
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- A \(J\)-function for marked point patterns
- Gaussian measures on L\(_p\) spaces, \(1\leq p \leq \infty\)
- Improving Ratio Estimators of Second Order Point Process Characteristics
- Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data
- Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
- A general central limit theorem and a subsampling variance estimator for α‐mixing point processes
- Stochastic and Integral Geometry
- Handbook of Spatial Statistics
- The second-order analysis of stationary point processes
- Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns
- Nonseparable, Stationary Covariance Functions for Space–Time Data
- Markov Point Processes and Their Applications
- An Introduction to the Theory of Point Processes
- Second‐order analysis of marked inhomogeneous spatiotemporal point processes: Applications to earthquake data
- A J‐function for Inhomogeneous Spatio‐temporal Point Processes
- Asymptotic Methods in Statistics of Random Point Processes
- A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions
- Asymptotic properties of an empiricalK-function for inhomogeneous spatial point processes
- Statistical Analysis and Modelling of Spatial Point Patterns
- An Introduction to the Theory of Point Processes
- Encyclopedia of Distances
- Gibbs point processes for studying the development of spatial-temporal stochastic processes.
This page was built for publication: Functional marked point processes: a natural structure to unify spatio-temporal frameworks and to analyse dependent functional data
