Heather Battey (University of Bristol)

Smooth projected density estimation

In this talk I will introduce a new class of estimators for multidimensional density estimation. The estimators are attractive in that they offer both flexibility and the possibility of incorporating structural constraints, whilst possessing a succinct representation that may be stored and evaluated easily. The latter property is of paramount importance when dealing with large datasets, which are now commonplace in many application areas. We show in a simulation study that the approach is universally unintimidated across a range of data generating mechanisms and often outperforms popular nonparametric estimators (including the kernel density estimator), even when structural constraints are not utilised. Moreover, its performance is shown to be somewhat robust to the choice of tuning parameters, which is an important practical advantage of our procedure.

Dr Jon Warren (University of Warwick)

Random matrices, Stochastic Growth models and the KPZ equation.

I will base this talk on two pieces of joint work. One with Peter Windridge, the other with Neil O'Connell. Firstly I will show you how the distribution of a largest eigenvalue of certain random matrix ( in fact having a Wishart distribution) arises also in a simple stochastic growth model. In fact this growth model belongs to a large universality class, which includes mathematical models for interfaces as diverse as the edge of a burning piece of paper, or a colony of bacteria on a petri dish. The KPZ equation is a stochastic partial differential equation that also belongs to this universality class, and in the work with Neil we set out to construct an analogue, for the KPZ equation, for the second, third and so on largest eigenvalues of the random matrix.

2.30 p.m. Prof. Michael Jordan, UC Berkeley
4.00 p.m. Prof. Henry Wynn, Department of Statistics, LSE
5.00 p.m. Drinks Reception

Presented by: Professor Valerie Isham (University College London; Past President of the Royal Statistical Society)

Rainfall is the driving force for many hydrological processes. As has been all too apparent in recent months, rainfall that cannot be absorbed or drained away causes major flooding disasters worldwide and flood defences must be designed to cope with extreme events. Soil moisture provides the dynamic link between climate, soil and vegetation, and impacts plant dynamics as well as other processes at a range of spatial scales. Historical rainfall data are, perhaps surprisingly, often not available at the temporal and spatial resolution needed for hydrological design. Climate change poses an additional challenge, as rainfall data under future climate scenarios are needed for design purposes. The talk, aimed at a general audience, will illustrate some of the approaches taken by statistical modellers to provide and use artificial rainfall data to address these issues.

Free attendance

There will be a reception after the lecture

Main contact point: paula.matthews@warwick.ac.uk

Downloads: 2013-005-08-valerie-isham.pdf

Simon French (University of Warwick)

Handling Uncertainties in Emergency Management

Emergency planners and managers continually encounter uncertainties. How should they face up to and deal with uncertainty? Some can be modelled, but many uncertainties are deep in that they are difficult to estimate because of a dearth of data. Moreover, it is usually necessary to communicate the uncertainty with a range of stakeholders. How should this been done without creating further scares. The talk will summarise my experiences in a number of fields from nuclear accidents such as Chernobyl and Fukushima, health scares and food safety incidents.