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Adaptive greedy algorithm for moderately large dimensions in kernel conditional density estimation
This paper studies the estimation of the conditional density f (x, ×) of...
06/28/2021 ∙ by Minh-Lien Jeanne Nguyen, et al. ∙
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Bayesian estimation of nonlinear Hawkes process
Multivariate point processes are widely applied to model event-type data...
03/31/2021 ∙ by Déborah Sulem, et al. ∙
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Uniform Deconvolution for Poisson Point Processes
We focus on the estimation of the intensity of a Poisson process in the ...
10/09/2020 ∙ by Anna Bonnet, et al. ∙
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Statistical deconvolution of the free Fokker-Planck equation at fixed time
We are interested in reconstructing the initial condition of a non-linea...
06/21/2020 ∙ by Mylène Maïda, et al. ∙
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Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation
Kernel density estimation is a well known method involving a smoothing p...
02/04/2019 ∙ by Suzanne Varet, et al. ∙
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Nonparametric Bayesian estimation of multivariate Hawkes processes
This paper studies nonparametric estimation of parameters of multivariat...
02/16/2018 ∙ by Judith Rousseau, et al. ∙
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Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation
We consider a stochastic individual-based model in continuous time to de...
10/25/2017 ∙ by Van Ha Hoang, et al. ∙
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