## Top 15 all-timers?

**N**ext year, I will organise a reading seminar for my graduate students so that they all read the “classics” of Statistics. Originally, I planned to use the book Biometrika: One Hundred Years by Mike Titterington and David Cox, but there are not enough papers in it, so I expanded the list and ended with the following. This is a poll so please vote for the 15 top papers all students should have read or add the missing ones. (Of course, those are supposed to be “classics”, so 2009 preprints are not appropriate!)

November 25, 2011 at 6:00 am

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June 26, 2010 at 6:02 pm

About EM algorithm, I’d add

@ARTICLE{Wu1983,

author = {C. F. J. Wu},

title = {On the Convergence Properties of the {EM} Algorithm},

journal = {Annals of Statistics},

year = {1983},

volume = {11},

pages = {95–103},

}

which is a must read (maybe more than the original Dempster et al).

I’d also add the overlooked and very, very short

@ARTICLE{Zehna1966,

author = {Zehna, Peter W.},

title = {Invariance of Maximum Likelihood Estimators},

journal = {Annals of Mathematical Statistics},

year = {1966},

volume = {37},

pages = {744},

}

and its very clean correction

@ARTICLE{Berk1967,

author = {Berk, R.H.},

title = {Review 1922 of Invariance of Maximum Likelihood Estimators by Peter W. Zehna},

journal = {Mathematical Reviews},

year = {1967},

volume = {33},

pages = {343-344}

}

who try to really define the maximum-likelihood estimator of $f(\theta)$ when $f$ is not one-to-one. Not as brilliant as the others in the list, of course, but worth having a look at.

In a totally different field, the following is a definite must-read in non linear design of experiments:

@ARTICLE{Box1959,

author = {Box, G.E.P. and Lucas, H.L.},

title = {Design of Experiments in Nonlinear Situations},

journal = {Biometrika},

year = {1959},

volume = {46},

pages = {77-90}

}

June 25, 2010 at 3:32 pm

Christian/Very good idea to include some classics in your teaching programme for graduate students.

What people have in mind about “classics” is actually a matter of debate. But in an historical perspective I would add at least one paper by RA Fisher, although most of them are difficult to read. May be “Theory of Statistical Estimation” (1922) as it laid the foundations of modern inference.

A more applied choice could be the 1936 one on mixtures with the famous “Iris” example. But this man has tackled some many subjects in statistics that we are spoilt for choice.