Shota Gugushvili

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Peter SpreijUniversiteit van Amsterdam, Korteweg-de Vries Institute for MathematicsVerified email at uva.nl
Bert Van EsAssociate professor university of AmsterdamVerified email at uva.nl
Moritz SchauerChalmers University of Technology | University of GothenburgVerified email at chalmers.se
Frank van der MeulenVrije Universiteit AmsterdamVerified email at vu.nl
Itai DattnerSenior Lecturer University of HaifaVerified email at stat.haifa.ac.il
Chris A.J. KlaassenEmeritus Professor of Mathematical Statistics, University of AmsterdamVerified email at uva.nl
Prof. Dr. Denis BelomestnyDuisburg-Essen UniversityVerified email at uni-due.de
A.W. van der VaartTU DelftVerified email at tudelft.nl
Eberhard VoitProfessor of Biomedical Engineering, Georgia TechVerified email at bme.gatech.edu
Ester MariucciHumboldt Universität zu BerlinVerified email at math.hu-berlin.de
Martin P BoerBiometris, Wageningen UR, The NetherlandsVerified email at wur.nl
Fred van EeuwijkProfessor in Applied Statistics, Wageningen University, The NetherlandsVerified email at wur.nl
Ryan Franklin McCormickStaff Scientist, Gro IntelligenceVerified email at gro-intelligence.com
George van VoornWageningen Universiteit and ResearchVerified email at wur.nl
Carel F.W. PeetersAssociate Professor of Statistics, Wageningen University & ResearchVerified email at wur.nl
Daniela Bustos-KortsFacultad de Ciencias Agrarias y Alimentarias, Universidad Austral de ChileVerified email at uach.cl
Sebastiano GrazziPhD student in applied mathematics, TU DelftVerified email at tudelft.nl
Carlos MessinaProfessor. Horticultural Sciences Department, University of FloridaVerified email at ufl.edu
Nicholas A. FriedenbergBiomathematician, Corteva AgriscienceVerified email at corteva.com
Sandra K TruongResearch Scientist at DuPont PioneerVerified email at pioneer.com

Shota Gugushvili

Wageningen University & Research

Verified email at yesdatasolutions.com - Homepage

Bayesian nonparametrics nonparametric statistics inference for stochastic processes inference for dynamical systems


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A kernel type nonparametric density estimator for decompounding B Van Es, S Gugushvili, P Spreij Bernoulli 13 (3), 672-694, 2007	83	2007
Nonparametric estimation of the characteristic triplet of a discretely observed Lévy process S Gugushvili Journal of Nonparametric Statistics 21 (3), 321-343, 2009	59	2009
-consistent parameter estimation for systems of ordinary differential equations: bypassing numerical integration via smoothing S Gugushvili, CAJ Klaassen Bernoulli 18 (3), 1061-1098, 2012	42	2012
Nonparametric inference for discretely sampled Lévy processes S Gugushvili Annales de l'IHP Probabilités et statistiques 48 (1), 282-307, 2012	41	2012
Dynamic programming and mean-variance hedging in discrete time S Gugushvili Georgian Mathematical Journal 10 (2), 237-246, 2003	25	2003
Deconvolution for an atomic distribution B Van Es, S Gugushvili, P Spreij Electronic Journal of Statistics 2, 265-297, 2008	22	2008
Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations* S Gugushvili, P Spreij Lithuanian Mathematical Journal 54 (2), 127-141, 2014	20	2014
Parametric inference for stochastic differential equations: a smooth and match approach S Gugushvili, P Spreij ALEA, Lat. Am. J. Probab. Math. Stat. 9 (2), 609-635, 2012	19	2012
Application of one-step method to parameter estimation in ODE models I Dattner, S Gugushvili Statistica Neerlandica, 2018	18*	2018
Nonparametric Bayesian inference for multidimensional compound Poisson processes S Gugushvili, F van der Meulen, P Spreij Modern Stochastics: Theory and Applications 2 (1), 1-15, 2015	18	2015
Fast and scalable non-parametric Bayesian inference for Poisson point processes S Gugushvili, F van der Meulen, M Schauer, P Spreij https://www.researchers.one/article/2019-06-6, 2019	17	2019
Fast and scalable non-parametric Bayesian inference for Poisson point processes S Gugushvili, F van der Meulen, M Schauer, P Spreij https://doi.org/10.5281/zenodo.1215900, 2018	17	2018
Bayesian linear inverse problems in regularity scales S Gugushvili, A van der Vaart, D Yan Ann. Inst. H. Poincaré Probab. Statist. 56 (3), 2081-2107, 2020	12	2020
A non-parametric Bayesian approach to decompounding from high frequency data S Gugushvili, F van der Meulen, P Spreij Statistical Inference for Stochastic Processes 21 (1), 53-79, 2018	11	2018
Weak convergence of the supremum distance for supersmooth kernel deconvolution B van Es, S Gugushvili Statistics & Probability Letters 78 (17), 2932-2938, 2008	11	2008
Separable nonlinear least-squares parameter estimation for complex dynamic systems I Dattner, S Gugushvili, H Ship, EO Voit https://arxiv.org/abs/1908.03717, 2019	9	2019
Nonparametric Bayesian inference for Gamma-type Lévy subordinators D Belomestny, S Gugushvili, M Schauer, P Spreij Communications in Mathematical Sciences 17 (3), 781-816, 2019	8*	2019
Deconvolution for an atomic distribution: rates of convergence S Gugushvili, B van Es, P Spreij Journal of Nonparametric Statistics 23 (4), 1003-1029, 2011	8	2011
Decompounding under Gaussian noise S Gugushvili arXiv preprint arXiv:0711.0719, 2007	8	2007
Nonparametric inference for partially observed Lévy processes S Gugushvili University of Amsterdam, 2008	7	2008

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