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We study negative association for mixed sampled point processes and show that negative association holds for such processes if a random number of their points fulfils the ultra log-concave (ULC) property. We connect the negative association property of point processes with directionally convex dependence ordering, and show some consequences of this property for mixed sampled and determinantal point processes. Some applications illustrate the general theory.

A sequence of finite point processes {Pn} is constructed in using a Markov sequence of points. Essentially, in the process Pn consisting of n events, the coordinates of these events are simply the first n points of a Markov sequence suitably scaled so that the average density of the process is independent of n. The second-order properties of Pn are discussed and sufficient conditions are found for Pn to converge in distribution to a Poisson process as n →∞. A simple example involving the cardioid distribution is described.

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On negative association of some finite point processes on general state spaces

A Markov construction for a multidimensional point process

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2 results

On negative association of some finite point processes on general state spaces

A Markov construction for a multidimensional point process