GIORGI Daphne

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Affiliations
  • 2014 - 2018
    Laboratoire de probabilités et modèles aléatoires
  • 2016 - 2017
    Sciences mathematiques de paris centre
  • 2016 - 2017
    Université Paris 6 Pierre et Marie Curie
  • 2020
  • 2018
  • 2017
  • 2015
  • Weak Error for Nested Multilevel Monte Carlo.

    Daphne GIORGI, Vincent LEMAIRE, Gilles PAGES
    Methodology and Computing in Applied Probability | 2020
    This article discusses MLMC estimators with and without weights, applied to nested expectations of the form E [f (E [F (Y, Z)|Y ])]. More precisely, we are interested on the assumptions needed to comply with the MLMC framework, depending on whether the payoff function f is smooth or not. A new result to our knowledge is given when f is not smooth in the development of the weak error at an order higher than 1, which is needed for a successful use of MLMC estimators with weights.
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