A general central limit theorem and a subsampling variance estimator for α‐mixing point processes

Christophe Ange Napoléon Biscio and Rasmus Waagepetersen

Scandinavian Journal of Statistics, 2019, vol. 46, issue 4, 1168-1190

Abstract: We establish a central limit theorem for multivariate summary statistics of nonstationary α‐mixing spatial point processes and a subsampling estimator of the covariance matrix of such statistics. The central limit theorem is crucial for establishing asymptotic properties of estimators in statistics for spatial point processes. The covariance matrix subsampling estimator is flexible and model free. It is needed, for example, to construct confidence intervals and ellipsoids based on asymptotic normality of estimators. We also provide a simulation study investigating an application of our results to estimating functions.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:46:y:2019:i:4:p:1168-1190

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