Prof. Isham Valerie, Statistical Science, University College London, UK (15:00-16:00)

Stochastic Epidemic Models: Approximations, structured populations and networks

Abstract: Epidemic models are developed as a means of gaining understanding about the dynamics of the spread of infection (human and animal pathogens, computer viruses etc.) and of rumours and other information. This understanding can then inform control measures to limit, or in some cases enhance, spread. Towards this goal, I will start from some simple stochastic transmission models, and describe some Gaussian approximations and their use for inference, illustrating this with data from a norovirus outbreak as well as from simulations. I will then discuss ways of incorporating population structure via metapopulations and networks, and the effects of network structure on epidemic spread. Finally I will briefly consider the extension to explicitly spatial mobile networks, as for example when computer viruses spread via short-range wireless or bluetooth connections.

Speaker: Spencer Wheatley, ETH Zurich, Switzerland

Title: The "endo-exo" problem in financial market price fluctuations, & the ARMA point process

The "endo-exo" problem -- i.e., decomposing system activity into exogenous and endogenous parts -- lies at the heart of statistical identification in many fields of science. E.g., consider the problem of determining if an earthquake is a mainshock or aftershock, or if a surge in the popularity of a youtube video is because it is "going viral", or simply due to high activity across the platform. Solution of this problem is often plagued by spurious inference (namely false strong interaction) due to neglect of trends, shocks and shifts in the data. The predominant point process model for endo-exo analysis in the field of quantitative finance is the Hawkes process. A comparison of this field with the relatively mature fields of econometrics and time series identifies the need to more rigorously control for trends and shocks. Doing so allows us to test the hypothesis that the market is "critical" -- analogous to a unit root test commonly done in economic time series -- and challenge earlier results. Continuing "lessons learned" from the time series field, it is argued that the Hawkes point process is analogous to integer valued AR time series. Following this analogy, we introduce the ARMA point process, which flexibly combines exo background activity (Poisson), shot-noise bursty dynamics, and self-exciting (Hawkes) endogenous activity. We illustrate a connection to ARMA time series models, as well as derive an MCEM (Monte Carlo Expectation Maximization) algorithm to enable MLE of this process, and assess consistency by simulation study. Remaining challenges in estimation and model selection as well as possible solutions are discussed.

[1] Wheatley, S., Wehrli, A., and Sornette, D. "The endo-exo problem in high frequency financial price fluctuations and rejecting criticality". To appear in Quantitative Finance (2018). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3239443

[2] Wheatley, S., Schatz, M., and Sornette, D. "The ARMA Point Process and its Estimation." arXiv preprint arXiv:1806.09948 (2018).

Daniel Rudolf, Institute for Mathematical Stochastics, Georg-August-Universität Göttingen

Title: Quantitative spectral gap estimate and Wasserstein contraction of simple slice sampling

Abstract: By proving Wasserstein contraction of simple slice sampling for approximate sampling of distributions determined by log-concave rotational invariant unnormalized densities we derive an explicit quantitative lower bound of the spectral gap. In particular, the lower bound of the spectral gap carries over to more general distributions depending only on the volume of the (super-)level sets of the unnormalized density.