Residual diagnostics for covariate effects in spatial point process models
MetadataShow full item record
For a spatial point process model in which the intensity depends on spatial covariates, we develop graphical diagnostics for validating the covariate effect term in the model, and for assessing whether another covariate should be added to the model. The diagnostics are point-process counterparts of the well-known partial residual plots (component-plus-residual plots) and added variable plots for generalized linear models. The new diagnostics can be derived as limits of these classical techniques under increasingly fine discretization, which leads to efficient numerical approximations. The diagnostics can also be recognized as integrals of the point process residuals, enabling us to prove asymptotic results. The diagnostics perform correctly in a simulation experiment. We demonstrate their utility in an application to geological exploration, in which a point pattern of gold deposits is modeled as a point process with intensity depending on the distance to the nearest geological fault. Online supplementary materials include technical proofs, computer code, and results of a simulation study. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Showing items related by title, author, creator and subject.
Modelling the co-occurence of Streptococcus pneumoniae with other bacterial and viral pathogens in the upper respiratory tractJacoby, P.; Watson, K.; Bowman, J.; Taylor, A.; Riley, T.; Smith, D.; Lehmann, Deborah (2007)Go to ScienceDirect® Home Skip Main Navigation Links Brought to you by: The University of Western Australia Library Login: + Register Athens/Institution Login Not Registered? - User Name: Password: ...
Nurunnabi, Abdul; Belton, David; West, Geoff (2012)Objectives: Surface reconstruction and fitting for geometric primitives and three Dimensional (3D) modeling is a fundamental task in the field of photogrammetry and reverse engineering. However it is impractical to get ...
Yick, John S. (2000)The influence of observations on the outcome of an analysis is of importance in statistical data analysis. A practical and well-established approach to influence analysis is case deletion. However, it has its draw-backs ...