Research Interests  

My main area of research is theoretical statistics. This treats the foundations and properties of methods of statistical inference. I am interested in how best to use information in the data to construct inferential statements about quantities of interest. A very simple example of this is the widely quoted `margin of error' in the reporting of polls, another is the ubiquitous `p-value' reported in medical and health studies. Much of my research considers how to ensure that these inferential statements are both accurate and effective at summarizing complex sets of data.

My particular focus over several years has been the development of extremely accurate approximations to p-values, developed from what is now called `higher order asymptotics'. I have published many papers with Don Fraser on this topic, and in 2007 Alessandra Brazzale, Anthony Davison, and I published a book called Applied Asymptotics, which aims to illustrate the application of this theoretical work on models useful for applied statistics. Recently Anthony Davison, Don Fraser, Nicola Sartori and I published a paper in the Journal fo the American Statistical Association extending these ideas to inference for vector parameters.

I have been working with students and colleagues on theoretical aspects of composite likelihood; an introduction is provided in a survey paper with Cristiano Varin and David Firth that appeared in Statistica Sinica in 2011. Recently graduated student Ximing Xu has made important contributions to our understanding of the properties of inference based on composite likelihood. With Xiaoping Shi and colleagues we have used saddlepoint methods to obtain improved approximations useful in inference for changepoints.