The paper gives first-order residual analysis for spatiotemporal point processes that is similar to the residual analysis that has been developed by Baddeley and co-workers for spatial point processes and also proposes principles for second-order residual analysis based on the viewpoint of martingales. Examples are given for both first- and second-order residuals. In particular, residual analysis can be used as a powerful tool in model improvement. Taking a spatiotemporal epidemic-type aftershock sequence model for earthquake occurrences as the base-line model, second-order residual analysis can be useful for identifying many features of the data that are not implied in the base-line model, providing us with clues about how to formulate bet...
For any point process in Rd that has a Papangelou conditional intensity λ, we define a random measur...
Spatial-temporal point process models are typically assessed using numerical summaries based on like...
Second-order methods provide a natural starting point for the analysis of spatial point process data...
Methods of examining the fit of multi-dimensional point process models using residual analysis are p...
We define residuals for point process models fitted to spatial point pattern data, and propose diagn...
Spatial, temporal, and spatio-temporal point processes, and in particular Poisson processes, are sto...
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the tra...
Spatial, temporal, and spatio-temporal point processes, and in particular Poisson processes, are sto...
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the tra...
This dissertation investigates the strengths and weaknesses of the current methods of residual analy...
Discussion of the paper "Residual analysis for spatial point processes" by A. Baddeley, M. Hazelton,...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
We define residuals for point process models fitted to spatial point pattern data, and we propose di...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
[[abstract]]For a spatial point process model in which the intensity depends on spatial covariates, ...
For any point process in Rd that has a Papangelou conditional intensity λ, we define a random measur...
Spatial-temporal point process models are typically assessed using numerical summaries based on like...
Second-order methods provide a natural starting point for the analysis of spatial point process data...
Methods of examining the fit of multi-dimensional point process models using residual analysis are p...
We define residuals for point process models fitted to spatial point pattern data, and propose diagn...
Spatial, temporal, and spatio-temporal point processes, and in particular Poisson processes, are sto...
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the tra...
Spatial, temporal, and spatio-temporal point processes, and in particular Poisson processes, are sto...
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the tra...
This dissertation investigates the strengths and weaknesses of the current methods of residual analy...
Discussion of the paper "Residual analysis for spatial point processes" by A. Baddeley, M. Hazelton,...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
We define residuals for point process models fitted to spatial point pattern data, and we propose di...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
[[abstract]]For a spatial point process model in which the intensity depends on spatial covariates, ...
For any point process in Rd that has a Papangelou conditional intensity λ, we define a random measur...
Spatial-temporal point process models are typically assessed using numerical summaries based on like...
Second-order methods provide a natural starting point for the analysis of spatial point process data...