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Department of Statistics

Publications

Get updates on new publications through my Google ScholarLink opens in a new window and Research GateLink opens in a new window.

Submitted

25. K. Pentland, M. Tamborrino, T. Sullivan (2022) Error bound analysis of the stochastic parareal algorithm. Preprint at arXiv:2211.05496.

24. A. Ahari, L. Alili, M. Tamborrino (2022) Boundary crossing problems and functional transformations for Ornstein-Uhlenbeck processesLink opens in a new window. Preprint at arXiv:2210.01658.

23. U. Picchini, M. Tamborrino (2022) Guided sequential ABC schemes for intractable Bayesian ModelsLink opens in a new window. Preprint at arXiv:2206.12235.

    Publications

    22. K. Pentland, M. Tamborrino, T.J. Sullivan, J. Buchanan, L. Appel (2023) GParareal: A time-parallel ODE solver using Gaussian process emulationLink opens in a new window, Stats. Comput. 33.

    21. K. Pentland, M. Tamborrino, D. Samaddar, L. Appel (2022) Stochastic parareal: An application of probabilistic methods to time-parallelisationLink opens in a new window. arXiv:2106.10139. To appear in SIAM J. Scient. Comput.

    20. E. Buckwar, A. Samson, M. Tamborrino, I. Tubikanec. Splitting methods for SDEs with locally Lipschitz drift. An illustration on the FitzHugh-Nagumo modelLink opens in a new window. Appl. Num. Math., 179, 191-220, 2022.

    19. I. Tubikanec, M. Tamborrino, P. Lansky, E. Buckwar. Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motionLink opens in a new window, J. Comput. Appl. Math., 406, 113951, 2022.

    18. M. Tamborrino, P. Lansky Shot noise, weak convergence and diffusion approximations. Link opens in a new windowPhysica D, 148, 132845, 2021. The R package for the exact simulation of shot noise processes, and non-Gaussian OU-processes (OU-Poisson, OU-Gamma, OU-Inverse Gaussian) is available on GitHubLink opens in a new window.

    17. M.J. Keeling, E. Hill, E. Gorsich, B. Penman, G. Guyver-Fletcher, A. Holmes, T. Leng, H. McKimm, M. Tamborrino, L. Dyson, M.Tildesley Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategiesLink opens in a new window. Plos Comput Biol. 171(1), e10086, 2021

      16. E. Buckwar, M. Tamborrino, I. Tubikanec. Spectral Density-Based and Measure-Preserving ABC for partially observed diffusion processes. A Demonstration on Hamiltonian SDEs,Link opens in a new window Stat. Comput., 30, 627-648, 2020. [Code]Link opens in a new window.
      I spoke about this work at the Bernoulli-IMS One World Symposium 2020Link opens in a new window. My pre-recorded 10minutes talk is available hereLink opens in a new window.

      15. G. D'Onofrio, P. Lansky, M. Tamborrino. Inhibition enhances the coherence in the Jacobi neuronal modelLink opens in a new window, Chaos Sol. Fract., 128, 108-113, 2019.

      14. G. D'Onofrio, M. Tamborrino, P. Lansky. The Jacobi diffusion process as a neuronal modelLink opens in a new window, Chaos 28, 103119, 1-10, 2018.

      13. M. Levakova, M. Tamborrino, L. Kostal and P. Lansky. Accuracy of rate coding: When shorter time window and higher spontaneous activity helpLink opens in a new window. Phys. Rev. E, 95(2): 022310, 2017.

      12. M. Tamborrino, S. Ditlevsen, B. Markussen and S. Kyllingsbæk. Gaussian counter models for Visual Identification of Briefly Presented, Mutually Confusable Single Stimuli in Pure Accuracy TasksLink opens in a new window. Math. Psychol., 79, 85-103, 2017.

      11.M. Levakova, M. Tamborrino, L. Kostal and P. Lansky. Presynaptic spontaneous activity enhances the accuracy of latency coding.Link opens in a new window Neural Comput., 28, 2162-2180, 2016.

      10. L. Sacerdote, M. Tamborrino and C. Zucca. First passage times of two-dimensional correlated processes: analytical results for the Wiener process and a numerical method for diffusion processesLink opens in a new window. J. Comput. Appl. Math., 296, 275-292, 2016.

      9 M. Tamborrino. Approximation of the first passage time density of a Brownian motion to an exponentially decaying threshold by two-piecewise linear threshold. Application to neuronal spiking activityLink opens in a new window. Math. Biosci. Eng., 13 (3), 613-629, 2016.

      8. J. C. Jacobsen, M. Tamborrino, P. Winkel, N. Haase, A. Perner, J. Wetterslev and C. Gluud. Count Data Analysis in Randomised Clinical TrialsLink opens in a new window. J. Biom Biostat. 6, 1, 2015.

      7. M. Levakova, M. Tamborrino, S. Ditlevsen and P. Lansky. A review of the methods for neuronal response latency estimationLink opens in a new window. BioSystem, 136, 23-34, 2015

      6. M. Tamborrino, S. Ditlevsen and P. Lansky. Parametric inference from hitting times for perturbed Brownian motionLink opens in a new window. Lifetime Data Anal. 21 (3): 331-352, 2015.

      5. Tamborrino, L. Sacerdote and M. Jacobsen. Weak convergence of marked point processes generated by crossings of multivariate jump processes. Application to neural network modelingLink opens in a new window. Physica D, 288: 45-52, 2014.

      4. M. Tamborrino, S. Ditlevsen and P. Lansky. Parametric inference of neuronal response latency in presence of a background signalLink opens in a new window. {\em BioSystems}, 112: 249--257, 2013.

      3. M. Tamborrino, S. Ditlevsen and P. Lansky. Identification of noisy response latencyLink opens in a new window. Phys. Rev. E, 86, 021128, 2012.

      2. L. Sacerdote, M. Tamborrino and C. Zucca. Detecting dependencies between spike trains of pairs of neurons through copulasLink opens in a new window. Brain Res., 1434: 243--256, 2012.

      1. L. Sacerdote and M. Tamborrino. Leaky Integrate and Fire models coupled through copulas: association properties of the Interspike IntervalsLink opens in a new window. J. Physiol., 53 (6): 396-406, 2010.