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Observations on Graduate Programs in Statistics II

1. Views Circa 1980-1988 “Prophecy is the most gratuitous form of error,” the novelist George Eliot caustically once observed. One often escapes criticism because predictions made in the past are soon either paid no heed or just plain forgotten. Indeed, why would investors continue with the same economic advisors after being given flawed, but albeit honest, counsel? However, one is obliged to review one’s own predictions, if nothing more than to ascertain where, why, and how one went astray. In looking back on a talk I had given in 1980 at a conference at the Ohio State University on the teaching of statistics graduate students, Geisser (1982), I was interested in recalling what I thought then would be important for statistics programs to emphasize. Firstly I had hoped that more statistics would be taught by statisticians in Statistics departments as opposed to other university departments but had little confidence that it would happen. I also felt that there was not enough recruitment of American graduate students to American Statistics graduate departments – a phenomenon occurring in mathematics and most other hard science disciplines as well. I also proposed increasing the stress on the philosophical or logical foundations of Statistics. I don’t think that happened. In some other areas I had some modest successes. Bayesian inference has made a substantial impact in the number of published journal articles and in the inclusion of graduate curricula as I had predicted, along with many others. The same is true of the area of model selection in partially replacing hypothesis testing and prediction being increasingly emphasized relative to parametric estimation, in which I was one of very few who foresaw that trend. I also suggested that one should carefully steer a program towards a middle course in terms of the poles of data analysis and mathematics. I believe now that I was harder on the contributions that mathematics makes to statistics then I should have been as it now appears we have deviated too far from this valuable tool in the direction of raw computation. I had, as almost everybody else, thought that the computer would have an enormous impact on statistical practice particularly in graphical displays and number crunching. It would facilitate more complicated and realistic modeling that previously had not been possible and be especially helpful for the computation involved in bootstrapping, cross-validation, sample reuse, jackknifing and the combining of discrete