Nonparametric Estimation of Spatial Segregation in a Multivariate Point Process: Bovine Tuberculosis in Cornwall, UK

DOI10.1111/j.1467-9876.2005.05373.xzbMath1490.62352OpenAlexW1975223360WikidataQ58852033 ScholiaQ58852033MaRDI QIDQ5757784

Peter A Durr, Pingping Zheng, Peter J. Diggle

Publication date: 7 September 2007

Published in: Journal of the Royal Statistical Society Series C: Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9876.2005.05373.x

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (14)

Semiparametric Multinomial Logistic Regression for Multivariate Point Pattern Data ⋮ The value of information for correlated GLMs ⋮ Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution ⋮ Analysing Multivariate Spatial Point Processes with Continuous Marks: A Graphical Modelling Approach ⋮ Approximate Bayesian inference for multivariate point pattern analysis in disease mapping ⋮ An evaluation of likelihood-based bandwidth selectors for spatial and spatiotemporal kernel estimates ⋮ Testing proportionality between the first-order intensity functions of spatial point processes ⋮ Cell-specific and post-hoc spatial clustering tests based on nearest neighbor contingency tables ⋮ Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm ⋮ Power of the Sequential Monte Carlo Test ⋮ Estimation of relative risk for events on a linear network ⋮ Regularized estimation for highly multivariate log Gaussian Cox processes ⋮ Residual Analysis for Spatial Point Processes (with Discussion) ⋮ Test for the first-order stationarity for spatial point processes in arbitrary regions

Cites Work

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• Model-Based Geostatistics
• Spatial Variation in Risk of Disease: A Nonparametric Binary Regression Approach
• Thin Plate Regression Splines

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