Statistical inference on stationary shot noise random fields

Antoine Lerbet ()
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Antoine Lerbet: Université de Tours

Statistical Inference for Stochastic Processes, 2023, vol. 26, issue 3, No 4, 580 pages

Abstract: Abstract We study the asymptotic behaviour of a stationnary shot noise random field. We use the notion of association to prove the asymptotic normality of the moments and a multidimensional version for the correlation functions. The variance of the moment estimates is detailed as well as their correlation. When the field is isotropic, the estimators are improved by reducing the variance. These results will be applied to the estimation of the model parameters in the case of a Gaussian kernel, with a focus on the correlation parameter. The asymptotic normality is proved and a simulation study is carried out.

Keywords: Shot noise random field; Parameter estimation; Statistical inference; Central limit theorem; Association; Isotropy (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11203-023-09294-y

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