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Central limit theorems for empirical product densities of stationary point processes. (English) Zbl 1306.60008
Summary: We prove the asymptotic normality of kernel estimators of second- and higher-order product densities (and of the pair correlation function) for spatially homogeneous (and isotropic) point processes observed on a sampling window $$W_n$$, which is assumed to expand unboundedly in all directions as $$n \to \infty$$. We first study the asymptotic behavior of the covariances of the empirical product densities under minimal moment and weak dependence assumptions. The proof of the main results is based on the Brillinger-mixing property of the underlying point process and certain smoothness conditions on the higher-order reduced cumulant measures. Finally, the obtained limit theorems enable us to construct $$\chi^2$$-goodness-of-fit tests for hypothetical product densities.

MSC:
 60F05 Central limit and other weak theorems 60G55 Point processes (e.g., Poisson, Cox, Hawkes processes) 60G10 Stationary stochastic processes 62M30 Inference from spatial processes 62G20 Asymptotic properties of nonparametric inference
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