I still remember the Annals of Statistics paper on Boosting by Friedman-Hastie-Tibshirani, and the comments on that same issues by other authors (including Freund and Schapire). At that time, clearly Boosting was viewed as a breakthrough in many respects: computationally feasible, an ensemble method, with excellent yet mysterious performance. Around the same time, SVM came of age, offering a framework underpinned by solid theory and with plenty of variants and applications.

That was in the marvelous 90s. In the past 15 years, it seems to me that a lot of Statistics has been a cleaning and detailing operation, but with few truly new views.

So I'll ask two questions:

1. Have I missed some revolutionary/seminal paper?
2. If not, are there new approaches that you think have the potential to change the viewpoint of statistical inference?

Rules:

1. One answer per post;
2. References or links welcome.

P.S.: I have a couple of candidates for promising breakthroughs. I will post them later.