My main research interests lie within mathematical statistics and include nonparametric inference, frequentist guarantees of Bayesian methods, statistical inverse problems (especially discretely observed stochastic processes) and empirical process theory.
I am also interested in and have briefly worked on exit problems for Lévy processes, completion of Bayesian networks and differential equations (their connections with probability and existence and uniqueness topics).
- Efficient nonparametric inference for discretely observed compound Poisson processes [ArXiv, doi]. Probability Theory and Related Fields 170 (2018), 475-523.
doi: 10.1007/s00440-017-0761-5. Matlab code available on request.
- Adaptive nonparametric estimation of compound Poisson processes robust to the discrete-observation scheme [ArXiv]. In review.
- Efficient nonparametric inference for discretely observed compound Poisson processes [doi]. PhD thesis (2017). doi: 10.17863/CAM.8528. Matlab code available on request.
- The two-sided exit problem for Lévy processes [.pdf]. CCA research project (2012).
- Gradient-based optimisation method [.pdf]. CCA research project (2012). Matlab code available on request.
- On the use of Bayesian networks to model stress events in banking [.pdf]. MSc thesis (2011). Partly published in “Denev, A. and Rebonato, R., 2014. Portfolio Management under Stress: A Bayesian-Net Approach to Coherent Asset Allocation. Cambridge University Press, Cambridge” [Amazon].