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Results: 106
Number of items: 106
  • 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
    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
  • 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
    Sperna Weiland, R. C. (2019). Essays on macro-financial risks. [Thesis, fully internal, Universiteit van Amsterdam]. Tinbergen Institute.
  • 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
  • Mandjes, M., & Spreij, P. (2017). A note on the central limit theorem for the idleness process in a one-sided reflected Ornstein–Uhlenbeck model. Statistica Neerlandica, 71(3), 225-235. https://doi.org/10.1111/stan.12108
  • Huang, G., Mandjes, M., & Spreij, P. (2016). Large deviations for Markov-modulated diffusion processes with rapid switching. Stochastic Processes and their Applications, 126(6), 1785-1818. https://doi.org/10.1016/j.spa.2015.12.005
  • Huang, G., Jansen, H. M., Mandjes, M., Spreij, P., & De Turck, K. (2016). Markov-modulated Ornstein-Uhlenbeck processes. Advances in Applied Probability, 48(1), 235-254. https://doi.org/10.1017/apr.2015.15
  • 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
  • Mandjes, M., & Spreij, P. (2016). Explicit Computations for Some Markov Modulated Counting Processes. In J. Kallsen, & A. Papapantoleon (Eds.), Advanced Modelling in Mathematical Finance: In Honour of Ernst Eberlein (pp. 63-89). (Springer Proceedings in Mathematics & Statistics; Vol. 189). Springer. https://doi.org/10.1007/978-3-319-45875-5_3
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