M
Marc Hoffmann
Researcher at Paris Dauphine University
Publications - 96
Citations - 2767
Marc Hoffmann is an academic researcher from Paris Dauphine University. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 26, co-authored 90 publications receiving 2465 citations. Previous affiliations of Marc Hoffmann include Centre national de la recherche scientifique & ENSAE ParisTech.
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Modelling microstructure noise with mutually exciting point processes
TL;DR: In this paper, the authors introduce a new stochastic model for the variations of asset prices at the tick-by-tick level in dimension 1 and 2 for a single asset and a pair of assets.
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Some limit theorems for Hawkes processes and application to financial statistics
TL;DR: In this article, a functional central limit theorem was proved for multivariate Hawkes processes observed over a time interval [ 0, T ] when T?? is a discrete scheme with mesh? over [ 0, T ] up to some further time shift.
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Division in Escherichia coli is triggered by a size-sensing rather than a timing mechanism.
Lydia Robert,Lydia Robert,Marc Hoffmann,Nathalie Krell,Stéphane Aymerich,Stéphane Aymerich,Jérôme Robert,Marie Doumic,Marie Doumic +8 more
TL;DR: It is demonstrated that a size-independent timer mechanism for division control, though theoretically possible, is quantitatively incompatible with the data and extremely sensitive to slight variations in the growth law.
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Statistical estimation of a growth-fragmentation model observed on a genealogical tree
TL;DR: In this paper, the authors present a nonparametric estimator of the division rate for a growing and dividing population modelled by a piecewise deterministic Markov branching tree, where the individuals split into two offsprings at a division rate B(x) that depends on their size x, whereas their size grows exponentially in time.
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Nonparametric estimation of scalar diffusions based on low frequency data
TL;DR: In this paper, the authors studied the problem of estimating the coefficients of a diffusion (X t, t ≥ 0), where the estimation is based on discrete data X n Δ, n = 0, 1,..., N. The sampling frequency is constant, and asymptotics are taken as the number N of observations tends to infinity.