A central limit theorem for the Euler integral of a Gaussian random field

Gregory Naitzat and Robert J. Adler

Stochastic Processes and their Applications, 2017, vol. 127, issue 6, 2036-2067

Abstract: Euler integrals of deterministic functions have recently been shown to have a wide variety of possible applications, including signal processing, data aggregation and network sensing. Adding random noise to these scenarios, as is natural in the majority of applications, leads to a need for statistical analysis, the first step of which requires asymptotic distribution results for estimators. The first such result is provided in this paper, as a central limit theorem for the Euler integral of pure, Gaussian, noise fields.

Keywords: Random field; Euler integral; Gaussian processes; Central limit theorem (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.spa.2016.09.007

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