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

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Abstrakt

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.

OriginalsprogEngelsk
TidsskriftScandinavian Journal of Statistics
Vol/bind46
Udgave nummer4
Sider (fra-til)1168-1190
Antal sider23
ISSN0303-6898
DOI
StatusUdgivet - 2019

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