Point process modelling and statistical inference

Over the last several decades, point process theory has become one of the major tools in applied probability and statistics. A point process is a random configuration of points in time, space og time-space, and possibly with marks and covariate information. As such, it is a flexible model for various real-life phenomena arising in many applications, including cosmology, physics, earth and atmospheric sciences, bioinformatics, ecology, risk management, financial econometrics and telecommunications. The development of new point process models, statistical and computational methods, motivated by important applications in science and technology, offers many challenging research problems:

Point processes in risk theory and ruin problems
Exit times and ruin probabilities: exact results
Almost surely continuous functions acting on point processes
Marked point processes in biological sequence analysis
Gene expression data and survival analysis
Inference for the additive hazards model
Marked point process regression
Flexible transformation models and goodness-of-fit
Dynamic treatment strategies
Repeated events and total time on test
Random sampling of communication networks
Stochastic channel modelling
Sampling patterns in the Lexis diagram
Point processes in extreme value theory for space-time models
Modelling large point pattern data
Mobile ad-hoc networks (MANETs)
Inhomogeneous Cox point processes
Permanent and determinat point processes
Residuals
Simulation-based Bayesian inference for spatial point processes