On the identifiability of interaction functions in systems of interacting particles

Zhongyang Li, Fei Lu, Mauro Maggioni, Sui Tang and Cheng Zhang

Stochastic Processes and their Applications, 2021, vol. 132, issue C, 135-163

Abstract: We address a fundamental issue in the nonparametric inference for systems of interacting particles: the identifiability of the interaction functions. We prove that the interaction functions are identifiable for a class of first-order stochastic systems, including linear systems with general initial laws and nonlinear systems with stationary distributions. We show that a coercivity condition is sufficient for identifiability and becomes necessary when the number of particles approaches infinity. The coercivity is equivalent to the strict positivity of related integral operators, which we prove by showing that their integral kernels are strictly positive definite by using Müntz type theorems.

Keywords: Interacting particle systems; Nonparametric regression; Positive-definite kernel; Positive operator; Identifiability (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spa.2020.10.005

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