Search results
Results: 24
Number of items: 24
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Belomestny, D., Gugushvili, S., Schauer, M., & Spreij, P. (2023). Weak solutions to gamma-driven stochastic differential equations. Indagationes Mathematicae, 34(4), 820-829. https://doi.org/10.1016/j.indag.2023.03.004 -
Gugushvili, S., van der Meulen, F., Schauer, M., & Spreij, P. (2023). Nonparametric Bayesian volatility learning under microstructure noise. Japanese Journal of Statistics and Data Science, 6(1), 551-571. https://doi.org/10.1007/s42081-022-00185-9
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Belomestny, D., Gugushvili, S., Schauer, M., & Spreij, P. (2022). Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations. Bernoulli, 28(4), 2151-2180. https://doi.org/10.3150/21-BEJ1413
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Gugushvili, S., van der Meulen, F., Schauer, M., & Spreij, P. (2020). Nonparametric bayesian estimation of a hölder continuous diffusion coefficient. Brazilian Journal of Probability and Statistics, 34(3), 537-559. https://doi.org/10.48550/arXiv.1706.07449, https://doi.org/10.1214/19-BJPS433
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Gugushvili, S., van der Meulen, F., Schauer, M., & Spreij, P. (2020). Fast and scalable non-parametric Bayesian inference for Poisson point processes. Researchers.One. https://researchers.one/articles/19.06.00001
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Belomestny, D., Gugushvili, S., Schauer, M., & Spreij, P. (2019). Nonparametric Bayesian inference for Gamma-type Lévy subordinators. Communications in Mathematical Sciences, 17(3), 781-816. https://doi.org/10.48550/arXiv.1804.11267, https://doi.org/10.4310/CMS.2019.v17.n3.a8
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Gugushvili, S., van der Meulen, F., Schauer, M., & Spreij, P. (2019). Bayesian wavelet de-noising with the caravan prior. ESAIM - Probability and Statistics, 23, 947-978. https://doi.org/10.1051/ps/2019019
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Gugushvili, S., van der Meulen, F., Schauer, M., & Spreij, P. (2019). Nonparametric Bayesian Volatility Estimation. In D. R. Wood, J. de Gier, C. E. Praeger, & T. Tao (Eds.), 2017 MATRIX Annals (pp. 279-302). (MATRIX Book Series; Vol. 2). Springer. https://doi.org/10.48550/arXiv.1801.09956, https://doi.org/10.1007/978-3-030-04161-8_19
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Gugushvili, S., van der Meulen, F., & Spreij, P. (2018). A non-parametric Bayesian approach to decompounding from high frequency data. Statistical Inference for Stochastic Processes, 21(1), 53-79. https://doi.org/10.1007/s11203-016-9153-1
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Gugushvili, S., & Spreij, P. (2016). Posterior contraction rate for non-parametric Bayesian estimation of the dispersion coefficient of a stochastic differential equation. ESAIM-Probability and Statistics, 20, 143-153. https://doi.org/10.1051/ps/2016008
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