Statistical Inference for Transformation Inhomogeneous Point Processes

Abstract.  Statistical inference for exponential inhomogeneous Markov point processes by transformation is discussed. It is argued that the inhomogeneity parameter can be estimated, using a partial likelihood based on an inhomogeneous Poisson point process. The inhomogeneity parameter can thereby be estimated without taking the interaction into account, which simplifies the statistical analysis considerably. Data analysis and simulation experiments support the results.     

A Note on Empirical Process Methods in the Theory of Poisson Point Processes

For a sample taken from an i.i.d. sequence of Poisson point processes with not necessarily finite unknown intensity measure the arithmetic mean is shown to be an estimator which is consistent uniformly on certain classes of functions. The method is a reduction to the case of finite intensity measure, which in turn can be dealt with using empirical process methods. A functional central limit theorem is also established in this context.     

Statistical Models for Birth and Death on a Flow: Local Absolute Continuity and Likelihood Ratio Processes

ABSTRACT. We consider statistical models for birth and death on a flow. We give necessary and sufficient conditions for local absolute continuity between laws of such processes on a suitable canonical path space, and prove that likelihood ratio processes do have a very simple form. Key tools are criteria for local absolute continuity of laws of l -point motions–for arbitrary l –on stochastic flows, under an assumption on the structure of infinitesimalcovariances.     

A J‐function for Inhomogeneous Spatio‐temporal Point Processes

We propose a new summary statistic for inhomogeneous intensity‐reweighted moment stationarity spatio‐temporal point processes. The statistic is defined in terms of the n‐point correlation functions of the point process, and it generalizes the J‐function when stationarity is assumed. We show that our statistic can be represented in terms of the generating functional and that it is related to the spatio‐temporal K‐function. We further discuss its explicit form under some specific model assumptions and derive ratio‐unbiased estimators. We finally illustrate the use of our statistic in practice. © 2014 Board of the Foundation of the Scandinavian Journal of Statistics