Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results.
Efron B., 1979, Bootstrap Methods: Another Look at the Jackknife, Annals of Statistics Vol. 7, No. 1, 1-26
Notes preparer: Haim Bar
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).