Local spatial log-Gaussian Cox processes for seismic data
From MaRDI portal
Publication:2106831
DOI10.1007/s10182-022-00444-wOpenAlexW4224433858WikidataQ114691586 ScholiaQ114691586MaRDI QIDQ2106831
Marianna Siino, Antonino D'Alessandro, Giada Adelfio, Nicoletta D'Angelo
Publication date: 19 December 2022
Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10182-022-00444-w
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Point process diagnostics based on weighted second-order statistics and their asymptotic properties
- Second order efficiency of minimum contrast estimators in a curved exponential family
- Likelihood analysis of spatial inhomogeneity for marked point patterns
- Approximation and simulation of the distributions of scan statistics for Poisson processes in higher dimensions
- Transforming spatial point processes into Poisson processes
- Nonparametric estimation of the dependence of a spatial point process on spatial covariates
- Bandwidth selection: Classical or plug-in?
- Including covariates in a space-time point process with application to seismicity
- On the measurability and consistency of minimum contrast estimates
- Spatiotemporal Prediction for Log-Gaussian Cox Processes
- Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
- Two-Step Estimation for Inhomogeneous Spatial Point Processes
- Statistical Methods for Spatial Data Analysis
- A Composite Likelihood Approach in Fitting Spatial Point Process Models
- Statistical Inference for Spatial Processes
- Handbook of Spatial Statistics
- A Kernel Method for Smoothing Point Process Data
- Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
- On Parameter Estimation and Goodness-of-Fit Testing for Spatial Point Patterns
- Maximum likelihood estimates of the fractal dimension for random spatial patterns
- A model for clustering
- The second-order analysis of stationary point processes
- Log Gaussian Cox Processes
- Model-Based Gaussian and Non-Gaussian Clustering
- Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns
- Shot noise Cox processes
- Markov Point Processes and Their Applications
- Practical Maximum Pseudolikelihood for Spatial Point Patterns
- Testing for Spatial Association Between a Point Process and Another Stochastic Process
- Parameter Estimation and Model Selection for Neyman‐Scott Point Processes
- Statistics for Spatial Data
- Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes
- An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes
- Statistical Analysis and Modelling of Spatial Point Patterns
- Assessing Spatial Point Process Models Using Weighted K-functions: Analysis of California Earthquakes
- Residual Analysis for Spatial Point Processes (with Discussion)
- Nearest-Neighbour Markov Point Processes and Random Sets
- An Introduction to the Theory of Point Processes
- Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm