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Results: 15
Number of items: 15
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Schauer, M., van der Meulen, F., & van Zanten, J. H. (2017). Guided proposals for simulating multi-dimensional diffusion bridges. Bernoulli, 23(4A), 2917-2950. https://doi.org/10.3150/16-BEJ833
  • Open Access
    Gugushvili, S., van der Meulen, F., & Spreij, P. (2015). Nonparametric Bayesian inference for multidimensional compound Poisson processes. Modern Stochastics : Theory and Applications, 2(1), 1-15. https://doi.org/10.15559/15-VMSTA20
  • van der Meulen, F., Schauer, M., & van Zanten, H. (2014). Reversible jump MCMC for nonparametric drift estimation for diffusion processes. Computational Statistics and Data Analysis, 71, 615-632. https://doi.org/10.1016/j.csda.2013.03.002
  • Open Access
    van der Meulen, F., & van Zanten, H. (2013). Consistent nonparametric Bayesian inference for discretely observed scalar diffusions. Bernoulli, 19(1), 44-63. https://doi.org/10.3150/11-BEJ385
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