From David Salsburg's book The lady tasting tea:
Although the reader may not believe it, literary style plays an important role in mathematical research. Some mathematical writers seem unable to produce articles that are easy to understand. Others seem to get a perverse pleasure out of generating many lines of symbolic notation so filled with detail that the general idea is lost in the picayune.
But some authors have the ability to display complicated ideas with such force and simplicity that the development appears to be obvious in their exposition. Only upon reviewing what has been learned does the reader realize the great power of the results. Such an author was Jerzy Neyman. It is a pleasure to read his papers. The ideas evolve naturally, the notation is deceptively simple, and the conclusions appear to be so natural that you find it hard to see why no one produced these results long before.
What are other specific examples of such well-written papers on statistics or machine learning?
The idea is to have a list of "this is how you should write" papers.
Please, try to provide:
Full bibliographic citation such as:
Carl E. Rasmussen, "The Infinite Gaussian Mixture Model" In Advances in Neural Information Processing Systems 12, Vol. 12 (2000)
In case of links, make them to publicly accessible repositories if possible (e.g. http://arxiv.org/).
A short, informal, comprehensible review on what is the paper about and why it is an example of a top well-written paper.