Parameter estimation in stochastic differential equations: An overview

By Jan Nygaard Nielsen, Henrik Madsen, Peter C. Young. 2000

130 citations recorded by the source corpus.

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Estimation of non-linear continuous time models for the heat exchange dynamics of building integrated photovoltaic modules (2008, methodology): “[3] has a review of the state of the art with respect to parameter estimation in discretely observed SDE’s, which shows that for models of the type in (1) and (2) only methods based on approximate non-linear filters provide a computationally feasible solution to the problem.”
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs (2019, methodology): “Moreover, Sørensen (2004) and Nielsen et al. (2000) provide extensive explanations of the more traditional approaches.”
Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “If the structure of the equation is known, say, from physical insight into a system, specialized parameter estimation techniques are available to fit a model to given observations [131].”
Parameter estimation in stochastic grey-box models (2004, background): “Nielsen et al. (2000) have recently reviewed the state of the art with respect to parameter estimation in discretely observed Itô stochastic diierential equations.”