Likelihood inference for complex data

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Modern technology has enabled the collection of large and complex sets of data, and these are being used to answer important research questions in many fields of science and social science. Statistical methods are used to understand both the structure and the noise in this data, and new methods are being rapidly developed by statisticians working in collaboration with biologists, physicists, epidemiologists, social scientists and many others. These new methods typically involve quite complex modelling, and are computationally intensive in their implementation. In many cases these new methods are defined algorithmically in each context. The theory of statistical inference provides us with a set of guidelines for tackling new problems, provides a framework for assessing approaches developed in particular contexts, and searches for the common structure in what may seem to be very diverse problems. Statistical theory has been very successfu in finding basic structure and suggesting new solutions, and as the field develops more and more sophisticated approaches to data, there is an accompanying need for understanding the basis for the analysis.