Goodness-of-fit test of the mark distribution in a point process with non-stationary marks

DOI10.1007/s11222-011-9263-yzbMath1252.62087OpenAlexW2002756961WikidataQ58993089 ScholiaQ58993089MaRDI QIDQ693324

Samuel Soubeyrand, Tomáš Mrkvička, Joël Chadoeuf

Publication date: 7 December 2012

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-011-9263-y

zbMATH Keywords

kernel estimationnuisance parametersmarked point processPoisson point processparametric bootstrapMaya sitesnon-stationary mark distribution

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Non-Markovian processes: hypothesis testing (62M07) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Uses Software

• SemiPar
• BayesDA
• bootstrap

Cites Work

• Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics
• Model-based geostatistics.
• Modelling marked point patterns by intensity-marked Cox processes
• Parametric bootstrapping with nuisance parameters
• Assessing goodness of fit of spatially inhomogeneous Poisson processes
• Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns
• Semiparametric Regression
• Statistical Analysis and Modelling of Spatial Point Patterns
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