Spatial point processes are mathematical models used to describe and analyse the
geometrical structure of patterns formed by objects that are irregularly or randomly …

Spatial point processes play a fundamental role in spatial statistics and today they are an
active area of research with many new applications. Although other published works …

Forestry statistics is an important field of applied statistics with a long tradition. Many forestry
problems can be solved by means of point processes or marked point processes. There, the" …

These days, an increasing amount of information can be obtained in graphical forms, such
as weather maps, soil samples, locations of nests in a breeding colony, microscopical slices …

Maximum likelihood parameter estimation and sampling from Bayesian posterior
distributions are problematic when the probability density for the parameter of interest …

We summarize and discuss the current state of spatial point process theory and directions
for future research, making an analogy with generalized linear models and random effect …

Background Sub-cellular structures interact in numerous direct and indirect ways in order to
fulfill cellular functions. While direct molecular interactions crucially depend on spatial …

We describe a simple way to construct new statistical models for spatial point pattern data.
Taking two or more existing models (finite Gibbs spatial point processes) we multiply the …

A stochastic model is applied to describe the spatial structure of a forest stand. We aim at
quantifying the strength of the competition process between the trees in terms of interaction …

Unsupervised empirical Bayesian multiple testing with external covariates Page 1 The Annals of
Applied Statistics 2008, Vol. 2, No. 2, 714–735 DOI: 10.1214/08-AOAS158 © Institute of …