Statistical inference on stationary shot noise random fields

被引：0
|
作者：
Lerbet, Antoine [1 ]
机构：
[1] Univ Tours, IDP UMR CNRS 7013, Parc Grandmont, F-37200 Tours, France
来源：
STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES | 2023年 / 26卷 / 03期
关键词：
Shot noise random field; Parameter estimation; Statistical inference; Central limit theorem; Association; Isotropy; CENTRAL-LIMIT-THEOREM; NORMAL CONVERGENCE; PERIMETER;
D O I：
10.1007/s11203-023-09294-y
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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.
引用
收藏
页码：551 / 580
页数：30
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