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166 Exciting timesvariability of a particular method for bandwidth choice, or the poor coverageproperties of a certain type of bootstrap confidence interval. I find it hard tobelieve that numerical methods, on their own, will ever have the capacity todeliver the level of intuition and insightful analysis, with such breadth andclarity, that theory can provide.Thus, in those early developments of methodology for function estimationand bootstrap methods, theory was providing unrivaled insights into newmethodology, as well as being ahead of the game of numerical practice. Methodswere suggested that were computationally impractical (e.g., techniquesfor bandwidth choice in the 1970s, and iterated bootstrap methods in the1980s), but they were explored because they were intrinsically attractive froman intuitive viewpoint. Many researchers had at least an impression that themethods would become feasible as the cost of computing power decreased, butthere was never a guarantee that they would become as easy to use as they aretoday. Moreover, it was not with a view to today’s abundant computing resourcesthat those computer-intensive methods were proposed and developed.To some extent their development was unashamedly an intellectual exercise,motivated by a desire to push the limits of what might sometime be feasible.In adopting this outlook we were pursuing a strong precedent. For example,Pitman (1937a,b, 1938), following the lead of Fisher (1935, p. 50), suggestedgeneral permutation test methods in statistics, well in advance of computingtechnology that would subsequently make permutation tests widely applicable.However, today the notion that we might discuss and develop computerintensivestatistical methodology, well ahead of the practical tools for implementingit, is often frowned upon. It strays too far, some colleagues argue,from the practical motivation that should underpin all our work.I’m afraid I don’t agree, and I think that some of the giants of the past,perhaps even Fisher, would concur. The nature of revolutions, be they instatistics or somewhere else, is to go beyond what is feasible today, and devisesomething remarkable for tomorrow. Those of us who have participated insome of the statistics revolutions in the past feel privileged to have beenpermitted free rein for our imaginations.AcknowledgementsI’m grateful to Rudy Beran, Anirban Dasgupta, Aurore Delaigle, and Jane-Ling Wang for helpful comments.