(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Just kidding. Here’s the real story. Susanna Makela writes:
A few of us want to start a journal club for the statistics PhD students. The idea is to read important papers that we might not otherwise read, maybe because they’re not directly related to our area of research/we don’t have time/etc.
What would you say are the top ten (or five) statistics papers that you think statistics PhD students should read?
What do all of you think? We’ve listed the most-cited statistics papers here. This list includes some classics but I don’t think they’re so readable.
For my recommendations I’d probably like a few papers that demonstrate good practice. Not necessarily the papers that introduce new methods, but the papers that show the use and development of good ideas.
And then there are the papers that introduce bad methods but are readable and thought-provoking. A lot of Tukey’s papers fit this category.
Anyway, let me think . . . I don’t really know where to start . . . let’s look at the index to my books . . .
Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: evidence from Social Security administrative records. American Economic Review 80, 313-336. I’m not saying this paper is perfect—what paper is?—but it’s a good one to read and discuss, treating it as an interesting study to criticize, not something to just be admired.
Rubin, D. B. (1980). Using empirical Bayes techniques in the law school validity studies (with discussion). Journal of the American Statistical Association 75, 801–827. This is good because there’s a lively discussion, also the paper itself is very solid.
Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In Handbook of Markov Chain Monte Carlo, ed. S. Brooks, A. Gelman, G. L. Jones, and X. L. Meng, 113–162. New York: Chapman & Hall. This is not an applied paper at all, it’s an expository paper on a computational method. But it’s just full of interesting ideas and excellent throwaway lines.
OK, three recommendations is enough from me.
What recommendations can the rest of you give?
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