Parameter estimation for discretely sampled stochastic heat equation driven by space-only noise revised

03/19/2020 ∙ by Igor Cialenco, et al. ∙ 0

The main goal of this paper is to build consistent and asymptotically normal estimators for the drift and volatility parameter of the stochastic heat equation driven by an additive space-only white noise when the solution is sampled discretely in the physical domain. We consider both the full space R and the bounded domain (0,π). First, we establish the exact regularity of the solution and its spatial derivative, which in turn, using power-variation arguments, allows building the desired estimators. Second, we propose two sets of estimators, based on sampling either the spatial derivative or the solution itself on a discrete space-time grid. Using the so-called Malliavin-Stein's method, we prove that these estimators are consistent and asymptotically normal as the spatial mesh-size vanishes. More importantly, we show that naive approximations of the derivatives appearing in the power-variation based estimators may create nontrivial biases, which we compute explicitly. We conclude with some numerical experiments that illustrate the theoretical results.



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