Substationarity for spatial point processes
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Publication:2418502
DOI10.1016/j.jmva.2018.11.001zbMath1417.62274arXiv1710.02864OpenAlexW2901309252WikidataQ128959465 ScholiaQ128959465MaRDI QIDQ2418502
Publication date: 27 May 2019
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1710.02864
semiparametric estimationkernel methodsnonstationarityspatial point processesintensity functionssubstationarity
Inference from spatial processes (62M30) Density estimation (62G07) Applications of statistics to environmental and related topics (62P12) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
Uses Software
Cites Work
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- A Kolmogorov-Smirnov type test for independence between marks and points of marked point processes
- Testing proportionality between the first-order intensity functions of spatial point processes
- A functional central limit theorem for weakly dependent sequences of random variables
- Proofs of the martingale FCLT
- Nonstationary covariance models for global data
- Central limit theorems for point processes
- Test for the first-order stationarity for spatial point processes in arbitrary regions
- Local linear regression smoothers and their minimax efficiencies
- Some results on Tchebycheffian spline functions and stochastic processes
- Stationarity Tests for Spatial Point Processes using Discrepancies
- A weighted estimating equation approach for inhomogeneous spatial point processes
- On Nonparametric Variance Estimation for Second-Order Statistics of Inhomogeneous Spatial Point Processes With a Known Parametric Intensity Form
- Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
- A COVARIANCE PARAMETER ESTIMATION METHOD FOR POLAR-ORBITING SATELLITE DATA
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- A KPSS Test for Stationarity for Spatial Point Processes
- Inference for Clustered Inhomogeneous Spatial Point Processes
- A Kernel Method for Smoothing Point Process Data
- Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
- The second-order analysis of stationary point processes
- Asymptotic Statistics
- Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns
- Estimating Pair Correlation Functions of Planar Cluster Processes
- An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes
- Spatio-temporal point processes, partial likelihood, foot and mouth disease
- Second‐Order Analysis of Inhomogeneous Spatial Point Processes Using Case–Control Data
- Fourier Analysis of Sub-Stationary Processes with a Finite Moment
- Stochastic Processes on a Sphere
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