I did not know Steve Fienberg well, but I met him several times and encountered his work on various occasions, which makes sense considering his research area was statistical modeling as applied to social science.
Fienberg’s most influential work must have been his books on the analysis of categorical data, work that was ahead of its time in being focused on the connection between models rather than hypothesis tests. He also wrote, with William Mason, the definitive paper on identification in age-period-cohort models, and he worked on lots of applied problems including census adjustment, disclosure limitation, and statistics in legal settings. The common theme in all this work is the combination of information from multiple sources, and the challenges involved in taking statistical inferences using these to make decisions in new settings. These ideas of integration and partial pooling are central to Bayesian data analysis, and so it makes sense that Fienberg made use of Bayesian methods throughout his career, and that he was a strong presence in the Carnegie Mellon statistics department, which has been one of the important foci of Bayesian research and education during the past few decades.
Fienberg’s CMU obituary quotes statistician and former Census Bureau director Bob Groves as saying,
Steve Fienberg’s career has no analogue in my [Groves’s] lifetime. . . . He contributed to advancements in theoretical statistics while at the same time nurturing the application of statistics in fields as diverse as forensic science, cognitive psychology, and the law. He was uniquely effective in his career because he reached out to others, respected them for their expertise, and perceptively saw connections among knowledge domains when others couldn’t see them. He thus contributed both to the field of statistics and to the broader human understanding of the world.
I’d say it slightly differently. I disagree that Fienberg’s career is unique in the way that Groves states. Others of Fienberg’s generation such as Don Rubin and Nan Laird have similarly made important theoretical or methodological contributions while also actively working on a broad variety of live applications. One can also point to researchers such as James Heckman and Lewis Sheiner who have come from outside to make important contributions to statistics while also doing important work in their own fields. And, to go to the next generation, I can for example point to my collaborators John Carlin and David Dunson, both of whom have had deep statistical insights while also contributing to the reform and development of their fields of application.
But please don’t take my qualification of Groves’s statement to be a criticism of Fienberg. Rather consider it as a plus. Fienberg is a model of an important way to be a statistician: to be someone deeply engaged with a variety of applied projects while at the same time making fundamental contributions to the core of statistics. Or, to put it another way, to work on statistical theory and methodology in the context of a deep engagement with a wide range of applications.
Lionel Trilling famously wrote this about George Orwell:
Orwell, by reason of the quality that permits us to say of him that he was a virtuous man, is a figure in our lives. He was not a genius, and this is one of the remarkable things about him. His not being a genius is an element of the quality that makes him what I am calling a figure. . . . if we ask what it is he stands for, what he is the figure of, the answer is: the virtue of not being a genius, of fronting the world with nothing more than one’s simple, direct, undeceived intelligence, and a respect for the powers one does have, and the work one undertakes to do. . . . what a relief! What an encouragement. For he communicates to us the sense that what he has done any one of us could do.
Or could do if we but made up our mind to do it, if we but surrendered a little of the cant that comforts us, if for a few weeks we paid no attention to the little group with which we habitually exchange opinions, if we took our chance of being wrong or inadequate, if we looked at things simply and directly, having only in mind our intention of finding out what they really are . . . He tells us that we can understand our political and social life merely by looking around us, he frees us from the need for the inside dope.
George Orwell is one of my heroes. I am not saying that Steve Fienberg is the George Orwell of statistics, whatever that would mean. What I do think is that the above traits identified by Trilling are related to what I admire most about Fienberg, and this is why I think it’s a fine accomplishment indeed for Fienberg to have not been a unique example of a statistician contributing both to theory and applications but an exemplar of this type. Laplace, Galton, and Fisher also fall in this category but none of us today can hope to match the scale of their contributions. Fienberg through his efforts changed the world in some small bit, as we all should hope to do.