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Inaugural lecture at Imperial College London: The Surprising Conditional Adventures of the Bootstrap Professor Alastair Young (Mathematics, Imperial College London) Monday 13 March 2006 18.00-19.00 Clore Lecture Theatre Abstract Many of the most significant advances in statistics over the last 50 years have been computational. Among these has been the development of bootstrap methods of inference. These provide estimates of statistical variability using empirical sampling models constructed from sample data and simulation from this empirical model, to replace the analytic calculations and approximations required by conventional approaches to inference. The methods can provide astonishingly accurate estimates, especially in the parametric context, when viewed from the perspective of repeated sampling. But this perspective is narrow, and a more appropriate viewpoint must take into account the demands of conditional inference, which argues that to be relevant statistical inference should be conditioned on certain features of the sample data being analysed. Conditional bootstrap simulation is, however, generally awkward. In this lecture we describe how particular bootstrap procedures, implemented without regard to the appropriate conditioning, can yield inference which respects that of an ideal conditional inference, to a surprisingly high degree. See also http://www.imperial.ac.uk/P7413.htm. Details for getting to the Mathematics Department: http://www.ma.ic.ac.uk/findus.php Kind regards Sofia Dr Sofia Olhede Lecturer in Statistics Department of Mathematics Imperial College London SW7 2AZ London UK Tel:+44 (0) 20 7594 8568 http://www.ma.imperial.ac.uk/~sco