Shota Gugushvili
Shota Gugushvili
Wageningen University & Research
Verified email at yesdatasolutions.com - Homepage
TitleCited byYear
A kernel type nonparametric density estimator for decompounding
B Van Es, S Gugushvili, P Spreij
Bernoulli 13 (3), 672-694, 2007
702007
Nonparametric estimation of the characteristic triplet of a discretely observed LÚvy process
S Gugushvili
Journal of Nonparametric Statistics 21 (3), 321-343, 2009
552009
-consistent parameter estimation for systems of ordinary differential equations: bypassing numerical integration via smoothing
S Gugushvili, CAJ Klaassen
Bernoulli 18 (3), 1061-1098, 2012
372012
Nonparametric inference for discretely sampled LÚvy processes
S Gugushvili
Annales de l'IHP ProbabilitÚs et statistiques 48 (1), 282-307, 2012
372012
Dynamic programming and mean-variance hedging in discrete time
S Gugushvili
Georgian Mathematical Journal 10 (2), 237-246, 2003
242003
Deconvolution for an atomic distribution
B Van Es, S Gugushvili, P Spreij
Electronic Journal of Statistics 2, 265-297, 2008
182008
Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations*
S Gugushvili, P Spreij
Lithuanian Mathematical Journal 54 (2), 127-141, 2014
172014
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
162015
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
152012
Application of one-step method to parameter estimation in ODE models
I Dattner, S Gugushvili
Statistica Neerlandica, 2018
10*2018
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
82018
Fast and scalable non-parametric Bayesian inference for Poisson point processes
S Gugushvili, F van der Meulen, M Schauer, P Spreij
https://arxiv.org/abs/1804.03616, 2018
82018
Weak convergence of the supremum distance for supersmooth kernel deconvolution
B van Es, S Gugushvili
Statistics & Probability Letters 78 (17), 2932-2938, 2008
82008
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
72018
Deconvolution for an atomic distribution: rates of convergence
S Gugushvili, B van Es, P Spreij
Journal of Nonparametric Statistics 23 (4), 1003-1029, 2011
72011
A note on non-parametric Bayesian estimation for Poisson point processes
S Gugushvili, P Spreij
arXiv preprint arXiv:1304.7353, 2013
62013
Decompounding under Gaussian noise
S Gugushvili
arXiv preprint arXiv:0711.0719, 2007
52007
Nonparametric Bayesian volatility estimation
S Gugushvili, F van der Meulen, M Schauer, P Spreij
Wood D., de Gier J., Praeger C., Tao T. (eds). 2017 MATRIX Annals. MATRIXá…, 2019
42019
Some thoughts on the asymptotics of the deconvolution kernel density estimator
B van Es, S Gugushvili
arXiv preprint arXiv:0801.2600, 2008
42008
Nonparametric inference for partially observed LÚvy processes
S Gugushvili
University of Amsterdam, 2008
42008
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Articles 1–20