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Paper of the Month

Please send comments for publication to statistics@uconn.edu.

November 2017

Once a month during the academic year our faculty will select a paper which we encourage our students to read and discuss. Papers featured in this section should be generally understood by graduate students, and will be selected either because of their impact, or historical value, or because they contain a useful (perhaps overlooked) techniques or results.

The inaugural “Paper of the Month” (November 2017) was selected to be Brad Efron’s paper titled “Bootstrap Methods: Another Look at the Jackknife”, published in 1979 in the Annals of Statistics (Vol. 7, No. 1, pages 1-26). It is hard to overstate the impact of this paper. It allows researchers to construct confidence intervals in many settings, even when there is no closed-form derivation of the standard deviation.

A glance at Efron’s Google scholar page shows that this work was already cited tens of thousands of times. In his column in the IMS Bulletin from November 17, 2016, Professor Xiao-Li Meng included this paper among his five selected “Nobel-Prize (NP) Worthy i.i.d Ideas in Statistics” and wrote: “[the bootstrap] certainly has made many researchers’ lives much easier”, and that it “has literally generated an industry of research on proving when it works, when it doesn’t, and how to make it work when its vanilla version fails.” For example, we may refer to the paper “Two Guidelines for Bootstrap Hypothesis Testing” by Peter Hall and Susan R. Wilson (Biometrics, Vol. 47, No. 2 (Jun., 1991), pp. 757-762)