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Steve Fienberg

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.


  1. Steve was my first dean at CMU. Too bad I was doing more logically oriented linguistics, natural language processing, and programming language semantics at the time. As a junior faculty member, I didn’t have much interaction with deans. And as a computational linguistics working on logically-oriented syntax, semantics, and programming languages, I didn’t have any professional interaction with him. By the time I became interested in statistics (and we hired Chris Manning to teach the intro class which changed my life), Steve had gone off to be a provost at York Univesity in Canada.

    I talked to Steve at a recent Brooklyn College of Law workshop on forensic linguistics, where I sat in for Andrew as an expert commentator. I was taken with Steve’s work on author identification; Mosteller and Wallace were so far ahead of their time it’s breathtaking and Steve extended their work in the Bayesian tradition. This work is pretty much completely unknown within computational linguistics and natural language processing circles.

  2. Jonathan says:

    Beautiful. Thanks.

    I never knew what Trilling meant by “genius”, partly because I think Orwell was a genius. This is a terrible example but I heard Howard Stern rag on Mike Judge about how simple it must have been to create Beavis & Butthead and Mike Judge, whose creativity has been abundantly demonstrated since then, said something like “but I did it”. I suppose Animal Farm looks easy, but he did it and often making something look easy is hard.

    In terms of legacy, I like to think of what we have and use as a building: it’s constructed out of materials provided by people we don’t know, put together by people we don’t know, and maybe it has a name attached to it, like a star architect or a star material (like Carrara marble), but each person’s contribution is in that building and their legacy is that we use it, we live in it, we enjoy it, we are inspired by it, we advance on it. If “genius” is fame, then genius is silly. Fermat is perhaps the best known mathematical name outside of Newton and perhaps a few others – certainly more well know than Leibniz – but he’s remembered for a marginal note that had to be wrong.

    • Thomas says:

      A few years ago the idea occurred to me that genius is a sort of compensation for honesty. People who are just honest about their beliefs and their reasons for holding them don’t have any particular need for genius, either within themselves or from others. The work of a genius is always to develop an elegant “workaround” for the limitations of human intelligence. Honest people just accept their limitations. That’s how I understand Trilling’s remark about Orwell. Proust, for example, was a “genius”; so, too, perhaps, was Henry Miller. But they weren’t, like Orwell, just speaking their minds plainly. (That may be because what they had to say couldn’t be said plainly, of course.)

  3. Keith O'Rourke says:

    I was a big fan of Steve and I always enjoyed my interactions with him (except strangely when he was the provost at York Univesity).

    My guess is “The common theme in all this work is the combination of information from multiple sources” initially came from his early interactions with Fred Mosteller and Richard Light and then it likely seemed just obvious.

  4. Thomas B says:

    Not to be the vinegar in the milk here, but on the specific issue of decomposing age-period-cohort effects, I seem to remember a thread on your blog which was critical of these models. As I remember it, you questioned the basic validity of such efforts. Is this correct? If so, would you point us to that thread?

  5. Herb Smith says:

    Found this late, just to add a few words about Steve Fienberg, whom I was privileged to know for many years. In graduate school, his text on analyzing cross-classified data was on many bookshelves. The paper cover, the thinness of the book — I always thought it looked like a coral snake. What a great book. Anyhow, I never had an interaction with Steve where he was anything other than super-smart, super-interesting, super-interested, and intellectually generous. Hearing him debate cohort modeling with David Freedman in Snowmass, Colorado in 1979 was quite an experience. Sad that they are both gone now, but that’s how it will be for all of us. Anyhow, an admirable scientist, an admirable scholar, and an admirable person….

    Andrew’s linked perspective on the subject of cohort analysis is an excellent one. I found this some years ago and find it apposite — I’ve used it in a talk or two.

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