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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n Yan, D.; Gugushvili, S.; and van der Vaart, A.\n\n\n \n \n \n \n \n Bayesian linear inverse problems in regularity scales with discrete observations.\n \n \n \n \n\n\n \n\n\n\n Sankhya A. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"BayesianPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{yan2024,\n\tabstract = {We obtain rates of contraction of posterior distributions in inverse problems with discrete observations. In a general setting of smoothness scales we derive abstract results for general priors, with contraction rates determined by discrete Galerkin approximation. The rate depends on the amount of prior concentration near the true function and the prior mass of functions with inferior Galerkin approximation. We apply the general result to non-conjugate series priors, showing that these priors give near optimal and adaptive recovery in some generality, Gaussian priors, and mixtures of Gaussian priors, where the latter are also shown to be near optimal and adaptive.},\n\tauthor = {Yan, Dong and Gugushvili, Shota and van der Vaart, Aad},\n\tdate = {2024/03/07},\n\tdate-added = {2024-03-08 10:00:48 +0100},\n\tdate-modified = {2024-03-08 10:00:48 +0100},\n\tdoi = {10.1007/s13171-024-00342-0},\n\tid = {Yan2024},\n\tisbn = {0976-8378},\n\tjournal = {Sankhya A},\n\ttitle = {Bayesian linear inverse problems in regularity scales with discrete observations},\n\turl = {https://doi.org/10.1007/s13171-024-00342-0},\n\tyear = {2024},\n\tbdsk-url-1 = {https://doi.org/10.1007/s13171-024-00342-0}}\n\n\n
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\n We obtain rates of contraction of posterior distributions in inverse problems with discrete observations. In a general setting of smoothness scales we derive abstract results for general priors, with contraction rates determined by discrete Galerkin approximation. The rate depends on the amount of prior concentration near the true function and the prior mass of functions with inferior Galerkin approximation. We apply the general result to non-conjugate series priors, showing that these priors give near optimal and adaptive recovery in some generality, Gaussian priors, and mixtures of Gaussian priors, where the latter are also shown to be near optimal and adaptive.\n
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\n  \n 2023\n \n \n (4)\n \n \n
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\n \n\n \n \n Dattner, I.; Gugushvili, S.; and Laskorunskyi, O.\n\n\n \n \n \n \n Model selection for ordinary differential equations: a statistical testing approach.\n \n \n \n\n\n \n\n\n\n arXiv e-prints,arXiv:2308.16438. aug 2023.\n \n\n\n\n
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@ARTICLE{2023arXiv230816438D,\n\tauthor = {{Dattner}, Itai and {Gugushvili}, Shota and {Laskorunskyi}, Oleksandr},\n\ttitle = {{Model selection for ordinary differential equations: a statistical testing approach}},\n\tjournal = {arXiv e-prints},\n\tkeywords = {Statistics - Methodology},\n\tyear = {2023},\n\tmonth = {aug},\n\teid = {arXiv:2308.16438},\n\tpages = {arXiv:2308.16438},\n\tdoi = {10.48550/arXiv.2308.16438},\n\tarchivePrefix = {arXiv},\n\teprint = {2308.16438},\n\tprimaryClass = {stat.ME},\n\tadsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230816438D},\n\tadsnote = {Provided by the SAO/NASA Astrophysics Data System}\n}\n\n\n
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\n \n\n \n \n Van Voorn, G. A.; Boer, M.; Truong, S. H.; Friedenberg, N. A.; Gugushvili, S.; Mccormick, R.; Bustos Korts, D.; Messina, C. D.; and Van Eeuwijk, F.\n\n\n \n \n \n \n A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations (I): A model library.\n \n \n \n\n\n \n\n\n\n Front. Plant Sci., 14. 2023.\n \n\n\n\n
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@article{frontiers2023a,\n\ttitle = {{A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations (I): A model library}},\n\tfjournal = {Frontiers in Plant Science},\n\tjournal = {Front. Plant Sci.},\n\tyear = {2023},\n\tissn = {0019-3577},\n\tdoi = {https://doi.org/10.3389/fpls.2023.1172359},\n\tVOLUME = {14},\n\tauthor =  {Van Voorn, George A. and  Boer, Martin  and Truong, Sandra H. and Friedenberg, Nicholas A. and Gugushvili, Shota and Mccormick, Ryan and Bustos Korts, Daniela and Messina, Carlos D.  and  Van Eeuwijk, Fred}\n}\n\n\n
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\n \n\n \n \n Belomestny, D.; Gugushvili, S.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n Weak solutions to gamma-driven stochastic differential equations.\n \n \n \n\n\n \n\n\n\n Indag. Math., 34(4): 820–829. 2023.\n \n\n\n\n
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@article{BELOMESTNY2023,\n\ttitle = {Weak solutions to gamma-driven stochastic differential equations},\n\tfjournal = {Indagationes Mathematicae},\n\tjournal = {Indag. Math.},\n\tyear = {2023},\n\tissn = {0019-3577},\n\tdoi = {https://doi.org/10.1016/j.indag.2023.03.004},\n\tVOLUME = {34},\n\tNUMBER = {4},\n\tPAGES = {820--829},\n\tauthor = {Belomestny, Denis and Gugushvili, Shota and Schauer, Moritz and  Spreij, Peter}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen , F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n Nonparametric Bayesian volatility learning under microstructure noise.\n \n \n \n\n\n \n\n\n\n Jpn. J. Stat. Data Sci., 6: 551–571 . 2023.\n \n\n\n\n
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@ARTICLE{microstructurepaper,\n\t author        = {{Gugushvili}, S. and {van der Meulen}, F. and {Schauer}, M. and {Spreij}, P.},\n\ttitle         = {{Nonparametric Bayesian volatility learning under microstructure noise}},\n\tJOURNAL = {{Jpn. J. Stat. Data Sci.}},\n\tFJOURNAL = {{Japanese Journal of Statistics and Data Science}},\n\tYEAR = {2023},\n\tvolume  = {6},\n\tpages   = {551--571 },\n\tISSN = {2520-8756},\n\tDOI = {10.1007/s42081-022-00185-9}\n}\n\n
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\n  \n 2022\n \n \n (2)\n \n \n
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\n \n\n \n \n Gugushvili, Shota; van der Meulen, F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n Julia code for nonparametric Bayesian volatility learning under microstructure noise.\n \n \n \n \n\n\n \n\n\n\n jul 2022.\n \n\n\n\n
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@software{shota_gugushvili_2022_6801410,\n\tauthor       = {{Gugushvili, Shota and\n\tvan der Meulen, Frank and\n\tSchauer, Moritz and Spreij, Peter}},\n\ttitle        = {{Julia code for nonparametric Bayesian volatility \n\tlearning under microstructure noise}},\n\tmonth        = {jul},\n\tyear         = {2022},\n\tpublisher    = {Zenodo},\n\tdoi          = {10.5281/zenodo.6801410},\n\turl          = {https://doi.org/10.5281/zenodo.6801410}\n}\n\n
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\n \n\n \n \n Belomestny, D.; Gugushvili, S.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations.\n \n \n \n \n\n\n \n\n\n\n Bernoulli, 28(4): 2151–2180. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"NonparametricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{zbMATH07594055,\n\tAuthor = {Belomestny, Denis and Gugushvili, Shota and Schauer, Moritz and Spreij, Peter},\n\tTitle = {Nonparametric {Bayesian} volatility estimation for gamma-driven stochastic differential equations},\n\tFJournal = {Bernoulli},\n\tJournal = {Bernoulli},\n\tISSN = {1350-7265},\n\tVolume = {28},\n\tNumber = {4},\n\tPages = {2151--2180},\n\tYear = {2022},\n\tLanguage = {English},\n\tDOI = {10.3150/21-BEJ1413},\n\tKeywords = {62Gxx,60Gxx,62Fxx},\n\tURL = {projecteuclid.org/journals/bernoulli/volume-28/issue-4/Nonparametric-Bayesian-volatility-estimation-for-gamma-driven-stochastic-differential-equations/10.3150/21-BEJ1413.full},\n\tzbMATH = {7594055}\n}\n\n
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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n Gugushvili, S.; and Peeters, C. F. W.\n\n\n \n \n \n \n Contributed discussion of ``Bayesian restricted likelihood methods: Conditioning on insufficient statistics in Bayesian regression\".\n \n \n \n\n\n \n\n\n\n Bayesian Anal., 16(14): 1450–1451. 2021.\n \n\n\n\n
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@article{ba2021,\n\ttitle = {{Contributed discussion of ``Bayesian restricted likelihood methods: Conditioning on insufficient statistics in Bayesian regression"}},\n\tfjournal = {Bayesian Analysis},\n\tjournal = {Bayesian Anal.},\n\tyear = {2021},\n\tdoi = {https://doi.org/10.1214/21-BA1257},\n\tVOLUME = {16},\n\tNUMBER = {14},\n\tPAGES = {1450--1451},\n\tauthor =  {Gugushvili, Shota and Peeters, Carel F. W.}\n}\n\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen, F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient.\n \n \n \n\n\n \n\n\n\n Braz. J. Probab. Stat., 34(3): 537–579. 2020.\n \n\n\n\n
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@ARTICLE{GuvaScSp2020,\n\tAUTHOR = {Gugushvili, Shota and van der Meulen, Frank and Schauer, Moritz and Spreij, Peter},\n\tTITLE = {{Nonparametric Bayesian estimation of a H\\"{o}lder continuous diffusion coefficient}},\n\tJOURNAL = {{Braz. J. Probab. Stat.}},\n\tFJOURNAL = {{Brazilian Journal of Probability and Statistics}},\n\tYEAR = {2020},\n\tVOLUME = {34},\n\tNUMBER = {3},\n\tPAGES = {537--579},\n\tISSN = {0103-0752},\n\tgroups = {Published},\n\tDOI = {10.1214/19-BJPS433}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Vaart , A.; and Yan, D.\n\n\n \n \n \n \n \n Bayesian linear inverse problems in regularity scales.\n \n \n \n \n\n\n \n\n\n\n Ann. Inst. H. Poincaré Probab. Statist., 56(3): 2081–2107. 2020.\n \n\n\n\n
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@Article{2018arXiv180208992G,\n\tauthor        = {{Gugushvili}, S. and {van der Vaart}, A. and {Yan}, D.},\n\ttitle         = {{Bayesian linear inverse problems in regularity scales}},\n\tjournal       = {{Ann. Inst. H. Poincar\\'{e} Probab. Statist.}},\n\tyear          = {2020},\n\tvolume  = {56},\n\tnumber  = {3},\n\tpages   = {2081--2107},\n\tdoi     = {10.1214/19-AIHP1029},\n\tgroups  = {Published},\n\turl     = {https://projecteuclid.org/euclid.aihp/1593137320}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; Mariucci, E.; and van der Meulen , F.\n\n\n \n \n \n \n \n Decompounding discrete distributions: A non-parametric Bayesian approach.\n \n \n \n \n\n\n \n\n\n\n Scand J. Statist., 47(2): 464–492. 2020.\n \n\n\n\n
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@ARTICLE{2019arXiv190311142G,\n\tauthor = {{Gugushvili}, Shota and {Mariucci}, Ester and {van der Meulen}, Frank},\n\ttitle = {{Decompounding discrete distributions: A non-parametric Bayesian approach}},\n\tjournal = {Scand J. Statist.},\n\tfjournal = {{Scandinavian Journal of Statistics}},\n\tyear = {2020},\t\n\tvolume  = {47},\n\tnumber  = {2},\n\tpages   = {464--492},\n\tdoi     = {10.1111/sjos.12413},\n\tgroups  = {Published},\n\turl     = {https://doi.org/10.1111/sjos.12413}\n}\n\n
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\n \n\n \n \n Dattner, I.; Gugushvili, S.; Ship, H.; and Voit, E. O.\n\n\n \n \n \n \n Separable nonlinear least-squares parameter estimation for complex dynamic systems.\n \n \n \n\n\n \n\n\n\n arXiv e-prints. Aug 2019.\n \n\n\n\n
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@ARTICLE{2019arXiv190803717D,\n\tauthor = {{Dattner}, Itai and {Gugushvili}, Shota and {Ship}, Harold and\n\t{Voit}, Eberhard O.},\n\ttitle = {{Separable nonlinear least-squares parameter estimation for complex dynamic systems}},\n\tjournal = {arXiv e-prints},\n\tkeywords = {Statistics - Methodology},\n\tyear = {2019},\n\tmonth = {Aug},\n\teid = {arXiv:1908.03717},\n\tarchivePrefix = {arXiv},\n\teprint = {1908.03717},\n\tprimaryClass = {stat.ME},\n\tadsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190803717D}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; Mariucci, E.; and van der Meulen, F.\n\n\n \n \n \n \n \n Bdd: Julia code for Bayesian decompounding of discrete distributions.\n \n \n \n \n\n\n \n\n\n\n mar 2019.\n \n\n\n\n
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@misc{bdd,\n\tauthor       = {Gugushvili, Shota and Mariucci, Ester and van der Meulen, Frank},\n\ttitle        = {{Bdd: Julia code for Bayesian decompounding of discrete distributions}},\n\tmonth        = {mar},\n\tyear         = {2019},\n\tdoi          = {10.5281/zenodo.2598802},\n\turl          = {https://doi.org/10.5281/zenodo.2598802}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen, F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n PointProcessInference 0.1.0 – Code and Julia package accompanying the article ``Gugushvili, van der Meulen, Schauer, Spreij (2018): Fast and scalable non-parametric Bayesian inference for Poisson point processes\" (http://arxiv.org/abs/1804.03616).\n \n \n \n \n\n\n \n\n\n\n 2019.\n \n\n\n\n
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@misc{pppjl,\n\ttitle \t\t= {{PointProcessInference 0.1.0 -- Code and Julia package accompanying the article ``Gugushvili, van der Meulen, Schauer, Spreij (2018): Fast and scalable non-parametric Bayesian inference for Poisson point processes" ({http://arxiv.org/abs/1804.03616})}},\n\tauthor \t\t= {Gugushvili, Shota and van der Meulen, Frank and Schauer, Moritz and Spreij, Peter},\n\tyear\t \t= {2019},\n\tdoi \t\t= {10.5281/zenodo.2591395},\n\turl \t\t= {https://doi.org/10.5281/zenodo.2591395},\n}\n\n
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\n \n\n \n \n Belomestny, D.; Gugushvili, S.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n Nonparametric Bayesian inference for Gamma-type Lévy subordinators.\n \n \n \n \n\n\n \n\n\n\n Commun. Math. Sci., 17(3): 781–816. 2019.\n \n\n\n\n
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@Article{2018arXiv180411267B,\n  author        = {{Belomestny}, D. and {Gugushvili}, S. and {Schauer}, M. and {Spreij}, P.},\n  title         = {{Nonparametric {B}ayesian inference for Gamma-type {L}\\'evy subordinators}},\n  fjournal       = {Communications in Mathematical Sciences},\n  journal       = {Commun. Math. Sci.},\n  year          = {2019},\n  volume         = {17},\n  number        = {3},\n  pages         = {781--816},\n  doi           = {10.4310/CMS.2019.v17.n3.a8},\n  url           = {http://dx.doi.org/10.4310/CMS.2019.v17.n3.a8},\n  groups        = {Published}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen , F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n Nonparametric Bayesian volatility estimation.\n \n \n \n \n\n\n \n\n\n\n In de Gier, J.; Praeger, C. E.; and Tao, T., editor(s), 2017 MATRIX Annals, pages 279–302, Cham, 2019. Springer International Publishing\n \n\n\n\n
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@InProceedings{2018arXiv180109956G,\n  author        = {{Gugushvili}, Shota and {van der Meulen}, Frank and {Schauer}, Moritz and {Spreij}, Peter},\n  title         = {{Nonparametric {B}ayesian volatility estimation}},\n  editor \t\t= {de Gier, Jan and Praeger, Cheryl E. and Tao, Terence},\n  bookTitle \t= {{2017 MATRIX Annals}},\n  year\t\t\t= {2019},\n  publisher\t\t= {Springer International Publishing},\n  address\t\t= {Cham},\n  pages\t\t\t= {279--302},\n  isbn\t\t\t= {978-3-030-04161-8},\n  doi\t\t\t= {10.1007/978-3-030-04161-8_19},\n  url\t\t\t= {https://doi.org/10.1007/978-3-030-04161-8_19}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen , F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n Fast and scalable non-parametric Bayesian inference for Poisson point processes.\n \n \n \n \n\n\n \n\n\n\n RESEARCHERS.ONE. june 2019.\n \n\n\n\n
\n\n\n\n \n \n \"FastPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{2018arXiv180403616G,\n  author        = {{Gugushvili}, S. and {van der Meulen}, F. and {Schauer}, M. and {Spreij}, P.},\n  title         = {{Fast and scalable non-parametric {B}ayesian inference for {P}oisson point processes}},\n  journal       = {RESEARCHERS.ONE},\n  year          = {2019},\n  month         = {june},\n  groups        = {Published},\n  url           = {https://www.researchers.one/article/2019-06-6}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen , F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n Bayesian wavelet de-noising with the caravan prior.\n \n \n \n \n\n\n \n\n\n\n ESAIM Probab. Stat., 23: 947–978. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"BayesianPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{refId0,\n\tauthor = {{Gugushvili}, Shota and {van der Meulen}, Frank and {Schauer}, Moritz and {Spreij}, Peter},\n\ttitle = {{Bayesian wavelet de-noising with the caravan prior}},\n\tdoi = {10.1051/ps/2019019},\n\tjournal = {ESAIM Probab. Stat.},\n\tyear = {2019},\n\tvolume = {23},\n\tpages = {947--978},\n\turl = {https://doi.org/10.1051/ps/2019019}\n}\n\n\n\n
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\n  \n 2018\n \n \n (6)\n \n \n
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\n \n\n \n \n Gugushvili, S.; van der Vaart, A. W.; and Yan, D.\n\n\n \n \n \n \n \n Bayesian inverse problems with partial observations.\n \n \n \n \n\n\n \n\n\n\n Transactions of A. Razmadze Mathematical Institute, 172(3, Part A): 388–403. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"BayesianPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{GUGUSHVILI2018388,\n  author  = {Gugushvili, S. and van der Vaart, Aad W., and Yan, Dong},\n  title   = {Bayesian inverse problems with partial observations},\n  journal = {Transactions of A. Razmadze Mathematical Institute},\n  year    = {2018},\n  volume  = {172},\n  number  = {3, Part A},\n  pages   = {388--403},\n  issn    = {2346-8092},\n  doi     = {10.1016/j.trmi.2018.09.002},\n  groups  = {Published},\n  url     = {https://doi.org/10.1016/j.trmi.2018.09.002}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen, F.; and Spreij, P.\n\n\n \n \n \n \n A non-parametric Bayesian approach to decompounding from high frequency data.\n \n \n \n\n\n \n\n\n\n Stat. Inference Stoch. Process., 21(1): 53–79. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{Gugushvili2018,\n  author   = {Gugushvili, Shota and van der Meulen, Frank and Spreij, Peter},\n  title    = {A non-parametric {B}ayesian approach to decompounding from high frequency data},\n  journal  = {Stat. Inference Stoch. Process.},\n  year     = {2018},\n  volume   = {21},\n  number   = {1},\n  pages    = {53--79},\n  issn     = {1387-0874},\n  doi      = {10.1007/s11203-016-9153-1},\n  fjournal = {Statistical Inference for Stochastic Processes. An International Journal Devoted to Time Series Analysis and the Statistics of Continuous Time Processes and Dynamical Systems},\n  groups   = {Published},\n  mrclass  = {62G07 (60G55 62F15 62G20)},\n  mrnumber = {3769832}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen, F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n \n Fast and scalable non-parametric Bayesian inference for Poisson point processes.\n \n \n \n \n\n\n \n\n\n\n apr 2018.\n \n\n\n\n
\n\n\n\n \n \n \"FastPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Misc{gugushvili_shota_2018_1215901,\n  author = {Gugushvili, Shota and van der Meulen, Frank and Schauer, Moritz and Spreij, Peter},\n  title  = {{Fast and scalable non-parametric Bayesian inference for Poisson point processes}},\n  month  = {apr},\n  year   = {2018},\n  doi    = {10.5281/zenodo.1215901},\n  groups = {Software},\n  url    = {https://doi.org/10.5281/zenodo.1215901}\n}\n\n
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\n \n\n \n \n Schauer, M.; and Gugushvili, S.\n\n\n \n \n \n \n \n MicrostructureNoise.\n \n \n \n \n\n\n \n\n\n\n may 2018.\n \n\n\n\n
\n\n\n\n \n \n \"MicrostructureNoisePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Misc{moritz_schauer_2018_1241024,\n  author = {Moritz Schauer and Shota Gugushvili},\n  title  = {MicrostructureNoise},\n  month  = {may},\n  year   = {2018},\n  doi    = {10.5281/zenodo.1241024},\n  groups = {Software},\n  url    = {https://doi.org/10.5281/zenodo.1241024}\n}\n\n
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\n \n\n \n \n Dattner, I.; and Gugushvili, S.\n\n\n \n \n \n \n \n Application of one-step method to parameter estimation in ODE models.\n \n \n \n \n\n\n \n\n\n\n Statistica Neerlandica, 72(2): 126–156. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"ApplicationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{doi:10.1111/stan.12124,\n  author  = {Dattner, Itai and Gugushvili, Shota},\n  title   = {Application of one-step method to parameter estimation in {ODE} models},\n  journal = {Statistica Neerlandica},\n  year    = {2018},\n  volume  = {72},\n  number  = {2},\n  pages   = {126--156},\n  doi     = {10.1111/stan.12124},\n  eprint  = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/stan.12124},\n  groups  = {Published},\n  url     = {https://onlinelibrary.wiley.com/doi/abs/10.1111/stan.12124}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; van der Meulen, F.; Schauer, M.; and Spreij, P.\n\n\n \n \n \n \n Code accompanying the paper \"Bayesian wavelet de-noising with the caravan prior\".\n \n \n \n\n\n \n\n\n\n 2018.\n \n\n\n\n
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@Misc{Gugushvili2018a,\n  author    = {Gugushvili, Shota and van der Meulen, Frank and Schauer, Moritz and Spreij, Peter},\n  title     = {Code accompanying the paper "Bayesian wavelet de-noising with the caravan prior"},\n  year      = {2018},\n  doi       = {10.5281/zenodo.1460345},\n  groups    = {Software},\n  publisher = {Zenodo}\n}\n\n\n
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\n  \n 2016\n \n \n (1)\n \n \n
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\n \n\n \n \n Gugushvili, S.; and Spreij, P.\n\n\n \n \n \n \n \n Posterior contraction rate for non-parametric Bayesian estimation of the dispersion coefficient of a stochastic differential equation.\n \n \n \n \n\n\n \n\n\n\n ESAIM Probab. Stat., 20: 143–153. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"PosteriorPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR3528621,\n  author   = {Gugushvili, Shota and Spreij, Peter},\n  title    = {Posterior contraction rate for non-parametric {B}ayesian estimation of the dispersion coefficient of a stochastic differential equation},\n  journal  = {ESAIM Probab. Stat.},\n  year     = {2016},\n  volume   = {20},\n  pages    = {143--153},\n  issn     = {1292-8100},\n  doi      = {10.1051/ps/2016008},\n  fjournal = {ESAIM. Probability and Statistics},\n  groups   = {Published},\n  mrclass  = {62G20 (60H10 62F15 62M05)},\n  mrnumber = {3528621},\n  url      = {https://doi.org/10.1051/ps/2016008}\n}\n\n
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\n  \n 2015\n \n \n (1)\n \n \n
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\n \n\n \n \n Gugushvili, S.; van der Meulen, F.; and Spreij, P.\n\n\n \n \n \n \n \n Nonparametric Bayesian inference for multidimensional compound Poisson processes.\n \n \n \n \n\n\n \n\n\n\n Mod. Stoch. Theory Appl., 2(1): 1–15. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"NonparametricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR3356921,\n  author     = {Gugushvili, Shota and van der Meulen, Frank and Spreij, Peter},\n  title      = {Nonparametric {B}ayesian inference for multidimensional compound {P}oisson processes},\n  journal    = {Mod. Stoch. Theory Appl.},\n  year       = {2015},\n  volume     = {2},\n  number     = {1},\n  pages      = {1--15},\n  issn       = {2351-6046},\n  doi        = {10.15559/15-VMSTA20},\n  fjournal   = {Modern Stochastics. Theory and Applications},\n  groups     = {Published},\n  mrclass    = {62G05 (60G51 60G57 62F15 62G07 62G20 62M99)},\n  mrnumber   = {3356921},\n  mrreviewer = {Zden\\v ek Hl\\'avka},\n  url        = {https://doi.org/10.15559/15-VMSTA20}\n}\n\n
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\n  \n 2014\n \n \n (2)\n \n \n
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\n \n\n \n \n Gugushvili, S.; and Spreij, P.\n\n\n \n \n \n \n \n Consistent non-parametric Bayesian estimation for a time-inhomogeneous Brownian motion.\n \n \n \n \n\n\n \n\n\n\n ESAIM Probab. Stat., 18: 332–341. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ConsistentPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR3333993,\n  author   = {Gugushvili, Shota and Spreij, Peter},\n  title    = {Consistent non-parametric {B}ayesian estimation for a time-inhomogeneous {B}rownian motion},\n  journal  = {ESAIM Probab. Stat.},\n  year     = {2014},\n  volume   = {18},\n  pages    = {332--341},\n  issn     = {1292-8100},\n  doi      = {10.1051/ps/2013039},\n  fjournal = {ESAIM. Probability and Statistics},\n  groups   = {Published},\n  mrclass  = {62G20 (62M05)},\n  mrnumber = {3333993},\n  url      = {https://doi.org/10.1051/ps/2013039}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; and Spreij, P.\n\n\n \n \n \n \n \n Nonparametric Bayesian drift estimation for multidimensional stochastic differential equations.\n \n \n \n \n\n\n \n\n\n\n Lith. Math. J., 54(2): 127–141. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"NonparametricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR3212631,\n  author     = {Gugushvili, Shota and Spreij, Peter},\n  title      = {Nonparametric {B}ayesian drift estimation for multidimensional stochastic differential equations},\n  journal    = {Lith. Math. J.},\n  year       = {2014},\n  volume     = {54},\n  number     = {2},\n  pages      = {127--141},\n  issn       = {0363-1672},\n  doi        = {10.1007/s10986-014-9232-1},\n  fjournal   = {Lithuanian Mathematical Journal},\n  groups     = {Published},\n  mrclass    = {62M05 (60H10 62F15 62G20)},\n  mrnumber   = {3212631},\n  mrreviewer = {Rosa Maria Mininni},\n  url        = {https://doi.org/10.1007/s10986-014-9232-1}\n}\n\n
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\n  \n 2013\n \n \n (2)\n \n \n
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\n \n\n \n \n Gugushvili, S.; Klaassen, C. A. J.; and van der Vaart, A. W.\n\n\n \n \n \n \n \n Editorial introduction [Special section: Parameter estimation for dynamical systems].\n \n \n \n \n\n\n \n\n\n\n Math. Biosci., 246(2): 281–282. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"EditorialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR3132049,\n  author   = {Gugushvili, Shota and Klaassen, Chris A. J. and van der Vaart, Aad W.},\n  title    = {Editorial introduction [{S}pecial section: {P}arameter estimation for dynamical systems]},\n  journal  = {Math. Biosci.},\n  year     = {2013},\n  volume   = {246},\n  number   = {2},\n  pages    = {281--282},\n  issn     = {0025-5564},\n  doi      = {10.1016/j.mbs.2013.07.015},\n  fjournal = {Mathematical Biosciences},\n  groups   = {Other},\n  mrclass  = {92-06},\n  mrnumber = {3132049},\n  url      = {https://doi.org/10.1016/j.mbs.2013.07.015}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; and Spreij, P.\n\n\n \n \n \n \n A note on non-parametric Bayesian estimation for Poisson point processes.\n \n \n \n\n\n \n\n\n\n ArXiv e-prints. apr 2013.\n \n\n\n\n
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@Article{2013arXiv1304.7353G,\n  author        = {{Gugushvili}, S. and {Spreij}, P.},\n  title         = {{A note on non-parametric {B}ayesian estimation for {P}oisson point processes}},\n  journal       = {ArXiv e-prints},\n  year          = {2013},\n  month         = {apr},\n  adsnote       = {Provided by the SAO/NASA Astrophysics Data System},\n  adsurl        = {http://adsabs.harvard.edu/abs/2013arXiv1304.7353G},\n  archiveprefix = {arXiv},\n  eprint        = {1304.7353},\n  groups        = {Older/unpublished},\n  keywords      = {Mathematics - Statistics Theory, 62G20 (Primary), 62M30 (Secondary)},\n  primaryclass  = {math.ST}\n}\n\n
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\n  \n 2012\n \n \n (3)\n \n \n
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\n \n\n \n \n Gugushvili, S.; and Spreij, P.\n\n\n \n \n \n \n Parametric inference for stochastic differential equations: a smooth and match approach.\n \n \n \n\n\n \n\n\n\n ALEA Lat. Am. J. Probab. Math. Stat., 9(2): 609–635. 2012.\n \n\n\n\n
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@Article{MR3069378,\n  author     = {Gugushvili, Shota and Spreij, Peter},\n  title      = {Parametric inference for stochastic differential equations: a smooth and match approach},\n  journal    = {ALEA Lat. Am. J. Probab. Math. Stat.},\n  year       = {2012},\n  volume     = {9},\n  number     = {2},\n  pages      = {609--635},\n  issn       = {1980-0436},\n  fjournal   = {ALEA. Latin American Journal of Probability and Mathematical Statistics},\n  groups     = {Published},\n  mrclass    = {62F12 (60H10 60J60 62G07 62G20 62M05)},\n  mrnumber   = {3069378},\n  mrreviewer = {Antonio Di Crescenzo}\n}\n\n
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\n \n\n \n \n Gugushvili, S.; and Klaassen, C. A. J.\n\n\n \n \n \n \n \n $\\sqrt n$-consistent parameter estimation for systems of ordinary differential equations: bypassing numerical integration via smoothing.\n \n \n \n \n\n\n \n\n\n\n Bernoulli, 18(3): 1061–1098. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"$\\sqrtPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2948913,\n  author   = {Gugushvili, Shota and Klaassen, Chris A. J.},\n  title    = {{$\\sqrt n$}-consistent parameter estimation for systems of ordinary differential equations: bypassing numerical integration via smoothing},\n  journal  = {Bernoulli},\n  year     = {2012},\n  volume   = {18},\n  number   = {3},\n  pages    = {1061--1098},\n  issn     = {1350-7265},\n  doi      = {10.3150/11-BEJ362},\n  fjournal = {Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability},\n  groups   = {Published},\n  mrclass  = {62F10 (34F05 62F12 62G20 93E24)},\n  mrnumber = {2948913},\n  url      = {https://doi.org/10.3150/11-BEJ362}\n}\n\n
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\n \n\n \n \n Gugushvili, S.\n\n\n \n \n \n \n \n Nonparametric inference for discretely sampled Lévy processes.\n \n \n \n \n\n\n \n\n\n\n Ann. Inst. Henri Poincaré Probab. Stat., 48(1): 282–307. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"NonparametricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2919207,\n  author     = {Gugushvili, Shota},\n  title      = {Nonparametric inference for discretely sampled {L}\\'evy processes},\n  journal    = {Ann. Inst. Henri Poincar\\'e Probab. Stat.},\n  year       = {2012},\n  volume     = {48},\n  number     = {1},\n  pages      = {282--307},\n  issn       = {0246-0203},\n  doi        = {10.1214/11-AIHP433},\n  fjournal   = {Annales de l'Institut Henri Poincar\\'e Probabilit\\'es et Statistiques},\n  groups     = {Published},\n  mrclass    = {62G07 (60G51 62G20)},\n  mrnumber   = {2919207},\n  mrreviewer = {Ross S. McVinish},\n  url        = {https://doi.org/10.1214/11-AIHP433}\n}\n\n
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\n  \n 2011\n \n \n (1)\n \n \n
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\n \n\n \n \n Gugushvili, S.; van Es, B.; and Spreij, P.\n\n\n \n \n \n \n \n Deconvolution for an atomic distribution: rates of convergence.\n \n \n \n \n\n\n \n\n\n\n J. Nonparametr. Stat., 23(4): 1003–1029. 2011.\n \n\n\n\n
\n\n\n\n \n \n \"DeconvolutionPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2854252,\n  author     = {Gugushvili, Shota and van Es, Bert and Spreij, Peter},\n  title      = {Deconvolution for an atomic distribution: rates of convergence},\n  journal    = {J. Nonparametr. Stat.},\n  year       = {2011},\n  volume     = {23},\n  number     = {4},\n  pages      = {1003--1029},\n  issn       = {1048-5252},\n  doi        = {10.1080/10485252.2011.576763},\n  fjournal   = {Journal of Nonparametric Statistics},\n  groups     = {Published},\n  mrclass    = {62G07 (62G20)},\n  mrnumber   = {2854252},\n  mrreviewer = {Mar\\'\\i a C. Iglesias-P\\'erez},\n  url        = {https://doi.org/10.1080/10485252.2011.576763}\n}\n\n
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\n  \n 2010\n \n \n (2)\n \n \n
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\n \n\n \n \n Gugushvili, S.; Klaassen, C.; and Spreij, P.\n\n\n \n \n \n \n \n Editorial introduction [Special issue on: statistical interence for Lévy processes with applications to finance].\n \n \n \n \n\n\n \n\n\n\n Stat. Neerl., 64(3): 255–256. 2010.\n \n\n\n\n
\n\n\n\n \n \n \"EditorialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2683459,\n  author   = {Gugushvili, Shota and Klaassen, Chris and Spreij, Peter},\n  title    = {Editorial introduction [{S}pecial issue on: statistical interence for {L}\\'evy processes with applications to finance]},\n  journal  = {Stat. Neerl.},\n  year     = {2010},\n  volume   = {64},\n  number   = {3},\n  pages    = {255--256},\n  issn     = {0039-0402},\n  doi      = {10.1111/j.1467-9574.2010.00459.x},\n  fjournal = {Statistica Neerlandica. Journal of the Netherlands Society for Statistics and Operations Research},\n  groups   = {Other},\n  mrclass  = {62-06},\n  mrnumber = {2683459},\n  url      = {https://doi.org/10.1111/j.1467-9574.2010.00459.x}\n}\n\n
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\n \n\n \n \n van Es, B.; and Gugushvili, S.\n\n\n \n \n \n \n \n Asymptotic normality of the deconvolution kernel density estimator under the vanishing error variance.\n \n \n \n \n\n\n \n\n\n\n J. Korean Statist. Soc., 39(1): 103–115. 2010.\n \n\n\n\n
\n\n\n\n \n \n \"AsymptoticPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2655814,\n  author   = {van Es, Bert and Gugushvili, Shota},\n  title    = {Asymptotic normality of the deconvolution kernel density estimator under the vanishing error variance},\n  journal  = {J. Korean Statist. Soc.},\n  year     = {2010},\n  volume   = {39},\n  number   = {1},\n  pages    = {103--115},\n  issn     = {1226-3192},\n  doi      = {10.1016/j.jkss.2009.04.007},\n  fjournal = {Journal of the Korean Statistical Society},\n  groups   = {Published},\n  mrclass  = {62G07 (62G20)},\n  mrnumber = {2655814},\n  url      = {https://doi.org/10.1016/j.jkss.2009.04.007}\n}\n\n
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\n  \n 2009\n \n \n (1)\n \n \n
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\n \n\n \n \n Gugushvili, S.\n\n\n \n \n \n \n \n Nonparametric estimation of the characteristic triplet of a discretely observed Lévy process.\n \n \n \n \n\n\n \n\n\n\n J. Nonparametr. Stat., 21(3): 321–343. 2009.\n \n\n\n\n
\n\n\n\n \n \n \"NonparametricPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2530929,\n  author     = {Gugushvili, Shota},\n  title      = {Nonparametric estimation of the characteristic triplet of a discretely observed {L}\\'evy process},\n  journal    = {J. Nonparametr. Stat.},\n  year       = {2009},\n  volume     = {21},\n  number     = {3},\n  pages      = {321--343},\n  issn       = {1048-5252},\n  doi        = {10.1080/10485250802645824},\n  fjournal   = {Journal of Nonparametric Statistics},\n  groups     = {Published},\n  mrclass    = {62G07 (60G51 62G20)},\n  mrnumber   = {2530929},\n  mrreviewer = {Ross S. McVinish},\n  url        = {https://doi.org/10.1080/10485250802645824}\n}\n\n
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\n  \n 2008\n \n \n (4)\n \n \n
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\n \n\n \n \n van Es, B.; and Gugushvili, S.\n\n\n \n \n \n \n \n Weak convergence of the supremum distance for supersmooth kernel deconvolution.\n \n \n \n \n\n\n \n\n\n\n Statist. Probab. Lett., 78(17): 2932–2938. 2008.\n \n\n\n\n
\n\n\n\n \n \n \"WeakPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2474384,\n  author     = {van Es, Bert and Gugushvili, Shota},\n  title      = {Weak convergence of the supremum distance for supersmooth kernel deconvolution},\n  journal    = {Statist. Probab. Lett.},\n  year       = {2008},\n  volume     = {78},\n  number     = {17},\n  pages      = {2932--2938},\n  issn       = {0167-7152},\n  doi        = {10.1016/j.spl.2008.05.002},\n  fjournal   = {Statistics \\& Probability Letters},\n  groups     = {Published},\n  mrclass    = {62G07},\n  mrnumber   = {2474384},\n  mrreviewer = {Marco Di Marzio},\n  url        = {https://doi.org/10.1016/j.spl.2008.05.002}\n}\n\n
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\n \n\n \n \n van Es, B.; Gugushvili, S.; and Spreij, P.\n\n\n \n \n \n \n \n Deconvolution for an atomic distribution.\n \n \n \n \n\n\n \n\n\n\n Electron. J. Stat., 2: 265–297. 2008.\n \n\n\n\n
\n\n\n\n \n \n \"DeconvolutionPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2399196,\n  author     = {van Es, Bert and Gugushvili, Shota and Spreij, Peter},\n  title      = {Deconvolution for an atomic distribution},\n  journal    = {Electron. J. Stat.},\n  year       = {2008},\n  volume     = {2},\n  pages      = {265--297},\n  issn       = {1935-7524},\n  doi        = {10.1214/07-EJS121},\n  fjournal   = {Electronic Journal of Statistics},\n  groups     = {Published},\n  mrclass    = {62G07 (62G20)},\n  mrnumber   = {2399196},\n  mrreviewer = {Marc Hoffmann},\n  url        = {https://doi.org/10.1214/07-EJS121}\n}\n\n
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\n \n\n \n \n van Es , B.; and Gugushvili, S.\n\n\n \n \n \n \n Some thoughts on the asymptotics of the deconvolution kernel density estimator.\n \n \n \n\n\n \n\n\n\n ArXiv e-prints. jan 2008.\n \n\n\n\n
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@Article{2008arXiv0801.2600V,\n  author        = {{van Es}, B. and {Gugushvili}, S.},\n  title         = {{Some thoughts on the asymptotics of the deconvolution kernel density estimator}},\n  journal       = {ArXiv e-prints},\n  year          = {2008},\n  month         = {jan},\n  adsnote       = {Provided by the SAO/NASA Astrophysics Data System},\n  adsurl        = {http://adsabs.harvard.edu/abs/2008arXiv0801.2600V},\n  archiveprefix = {arXiv},\n  eprint        = {0801.2600},\n  groups        = {Older/unpublished},\n  keywords      = {Statistics - Methodology, 62G07},\n  primaryclass  = {stat.ME}\n}\n\n
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\n \n\n \n \n Gugushvili, S.\n\n\n \n \n \n \n \n Nonparametric Inference for Partially Observed Lévy Processes.\n \n \n \n \n\n\n \n\n\n\n Ph.D. Thesis, University of Amsterdam, 2008.\n \n\n\n\n
\n\n\n\n \n \n \"NonparametricPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@PhdThesis{gugushvili_thesis,\n  author = {Gugushvili, Shota},\n  title  = {Nonparametric {I}nference for {P}artially {O}bserved {L}\\'evy {P}rocesses},\n  school = {University of {A}msterdam},\n  year   = {2008},\n  groups = {PhD thesis},\n  url    = {http://hdl.handle.net/11245/1.279647}\n}\n\n\n
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\n  \n 2007\n \n \n (2)\n \n \n
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\n \n\n \n \n van Es, B.; Gugushvili, S.; and Spreij, P.\n\n\n \n \n \n \n \n A kernel type nonparametric density estimator for decompounding.\n \n \n \n \n\n\n \n\n\n\n Bernoulli, 13(3): 672–694. 2007.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{MR2348746,\n  author     = {van Es, Bert and Gugushvili, Shota and Spreij, Peter},\n  title      = {A kernel type nonparametric density estimator for decompounding},\n  journal    = {Bernoulli},\n  year       = {2007},\n  volume     = {13},\n  number     = {3},\n  pages      = {672--694},\n  issn       = {1350-7265},\n  doi        = {10.3150/07-BEJ6091},\n  fjournal   = {Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability},\n  groups     = {Published},\n  mrclass    = {62G07 (62G20)},\n  mrnumber   = {2348746},\n  mrreviewer = {Cyrille J. Joutard},\n  url        = {https://doi.org/10.3150/07-BEJ6091}\n}\n\n
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\n \n\n \n \n Gugushvili, S.\n\n\n \n \n \n \n Decompounding under Gaussian noise.\n \n \n \n\n\n \n\n\n\n ArXiv e-prints. November 2007.\n \n\n\n\n
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@Article{2007arXiv0711.0719G,\n  author        = {{Gugushvili}, S.},\n  title         = {{Decompounding under {G}aussian noise}},\n  journal       = {ArXiv e-prints},\n  year          = {2007},\n  month         = nov,\n  adsnote       = {Provided by the SAO/NASA Astrophysics Data System},\n  adsurl        = {http://adsabs.harvard.edu/abs/2007arXiv0711.0719G},\n  archiveprefix = {arXiv},\n  eprint        = {0711.0719},\n  groups        = {Older/unpublished},\n  keywords      = {Mathematics - Statistics, 62G07, 62G20}\n}\n\n
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\n  \n 2003\n \n \n (1)\n \n \n
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\n \n\n \n \n Gugushvili, S.\n\n\n \n \n \n \n Dynamic programming and mean-variance hedging in discrete time.\n \n \n \n\n\n \n\n\n\n Georgian Math. J., 10(2): 237–246. 2003.\n Dedicated to the memory of Professor Revaz Chitashvili\n\n\n\n
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@Article{MR2009973,\n  author     = {Gugushvili, S.},\n  title      = {Dynamic programming and mean-variance hedging in discrete time},\n  journal    = {Georgian Math. J.},\n  year       = {2003},\n  volume     = {10},\n  number     = {2},\n  pages      = {237--246},\n  issn       = {1072-947X},\n  note       = {Dedicated to the memory of Professor Revaz Chitashvili},\n  fjournal   = {Georgian Mathematical Journal},\n  groups     = {Published},\n  mrclass    = {91B28 (60H30 90C39)},\n  mrnumber   = {2009973},\n  mrreviewer = {Diethard Pallaschke}\n}\n\n
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