Thoughts About Statistics

Marron Personal Paragraph (July 2013):

J. S. Marron is the Amos Hawley Distinguished Professor of Statistics and Operations Research, at the University of North Carolina, Chapel Hill.  He received the B. S. degree from the University of California at Davis, and the Ph. D. from the University of California at Los Angeles.  Marron has held the positions of Assistant, Associate and Full Professor with the University of North Carolina, Chapel Hill, and is also Professor of Biostatistics and Adjunct Professor of Computer Science and Member of the Lineberger Comprehensive Cancer Center.  He was a founding Associate Director of the Statistical and Applied Mathematical Sciences Institte (SAMSI).  He has also served as Mary Upson Distinguished Professor of Operations Research at Cornell University, and held 13 other visiting positions in four countries.  Marron is an elected Fellow of the American Statistical Institute and the Institute for Mathematical Statistics, and an elected Member of the International Statistical Institute.  Marron has served as Associate Editor for the Annals of Statistics, the Journal of the American Statistical Association, the Journal of Nonparametric Statistics, Computational Statistics and Test.  He is currently Associate Editor of the Electronic Journal of Statistics.  Marron has presented the Theory and Methods Invited Paper for the Journal of the American Statistical Association, been the Institute of Mathematical Statistics Medallion Lecturer, and presented the S. N. Roy Memorial Lecture at the University of Calcutta.  He has delivered the Bradley Lecture at the University of Georgia, and the Information Science and Technology Center Distinguished Lecture at Colorado State University.



Contribution to Larry Wasserman’s Blog (May 2013):

My colleagues and I have lately been discussing “Big Data”, and your blog:

was mentioned.

Not surprisingly you’ve got some interesting ideas there.  Here come some of my own thoughts on the matter.

First should one be pessimistic?  I am not so sure.  For me exhibit A is my own colleagues.  When such things came up in the past (and I believe that this HAS happened, see the discussion below) my (at that time senior) colleagues were rather arrogantly ignorant.  Issues such as you are raising were blatantly pooh poohed, if they were ever considered at all.  However, this time around, I am seeing a far more different picture.  My now mostly junior colleagues are taking this very seriously, and we are currently engaged in major discussion as to what we are going to do about this in very concrete terms such as course offerings, etc.  In addition, while some of my colleagues think in terms of labels such as “applied statistician”, “theoretical statistician” and “probabilist”, everybody across the board is jumping in.  Perhaps this is largely driven by an understanding that universities themselves are in a massive state of flux, and that one had better be a player, or else be totally left behind.  But it sure looks better than some of the attitudes I saw earlier on in my career.

Now about the bigger picture.  I think there is an important history here that you are totally ignoring.  In particular, I view “Big Data” as just the latest manifestation of a cycle that has been rolling along for quite a long time.   Actually I have been predicting the advent of something of this type for quite a while (although I could not predict the name, nor the central idea).

Here comes a personally slanted (certainly over-simplified) view of what I mean here.  Think back on the following set of “exciting breakthroughs”:

– Statistical Pattern Recognition
– Artificial Intelligence
– Neural Nets
– Data Mining
– Machine Learning

Each of these was started up in EE/CS.  Each was the fashionable hot topic (considered very sexy and fresh by funding agencies) of its day.  Each was initially based on usually one really cool new idea, which was usually far outside of what folks working in conventional statistics had any hope (well certainly no encouragement from the statistical community) of dreaming up.  I think each attracted much more NSF funding than all of statistics ever did, at any given time.  A large share of the funding was used for re-invention of ideas that already existed in statistics (but would get a sexy new name).  As each new field matured, there came a recognition that in fact much was to be gained by studying connections to statistics, so there was then lots of work “creating connections”.

Now given the timing of these, and how they each have played out, over time, it had been clear to me for some time that we were ripe for the next one.  So the current advent of Big Data is no surprise at all.  Frankly I am a little disappointed that there does not seem to be any really compelling new idea (e.g. as in neural nets or the kernel embedding idea that drove machine learning).  But I suspect that the need for such a thing to happen to keep this community properly funded has overcome the need for an exciting new idea. Instead of new methodology, this seems to be more driven by parallelism and cloud computing.  Also I seem to see larger applied math buy-in than there ever was in the past.  Maybe this is the new parallel to how optimization has appeared in a major way in machine learning.

Next, what should we do about it?  Number one of course is to get engaged, and as noted above, I am heartened at least at my own local level as discussed above.

I generally agree with your comment about funding, and I can think of ways to sell statistics.  For example, we should make the above history clear to funding agencies, and point out that in each case there has been a huge waste of resources on people doing a large amount of rediscovery.  In most of those areas, by the time the big funding hits, the main ideas are already developed so the funding really just keeps lots of journeymen doing lots of very low impact work, with large amounts of rediscovery of things already known in the statistical community.  The sell could be that a better funded statistical community would be a more efficient way to get such things done without all of this highly funded re-discovery.

But before making such a case, I suggest that is it important to face up to our own shortcomings, from the perspective of funding agencies.  I can see a strong reason why it DOES NOT make sense to fund our community better.  That is our community wide aversion to new ideas.  While I love working with statistical concepts, and have a personal love of new ideas, it has not escaped my notice that I have always been in something of a minority in that regard.  We not only do not choose to reward creativity, we often tend to squelch it.  I still remember the first time I applied for an NSF grant.  I was ambitious, and the reviews I got back said the problem was interesting, but I had no track record, the reviewers were skeptical of me, and I did not get funded.  This was especially frustrating as by the time I got those reviews I had solved the stated problem.  It would be great if that could be regarded as an anomaly of the past when folks may have been less enlightened than now.  However, I have direct evidence that this is not true. Unfortunately exactly that cycle repeated itself for one of my former students on this very last NSF cycle.

What should we do to deserve more funding?  Somehow we need a bigger tent, which is big enough to include the creative folks who will be coming up with the next really big ideas (big enough to include the folks who are going to spawn the next new community, such as those listed above).  This is where research funding should really be going to be most effective.

Maybe more important, we need to find a way to create a statistical culture that reveres new ideas, instead of fearing and shunning them.