What are some interesting and well-written applied statistics papers? What are some good papers describing applications of statistics that would be fun and informative to read? Just to be clear, I'm not really looking for papers describing new statistical methods (e.g., a paper on least angle regression), but rather papers describing how to solve real-world problems.
For example, one paper that would fit what I'm looking is the climate paper from the second Cross-Validated Journal Club. I'm kind of looking for more statistics-ish papers, rather than machine learning papers, but I guess it's kind of a fuzzy distinction (I'd classify the Netflix Prize papers as a bit borderline, and a paper on sentiment analysis as something I'm not looking for).
I'm asking because most of the applications of statistics I've seen are either the little snippets you seen in textbooks, or things related to my own work, so I'd like to branch out a bit.
 A: On a wider level I would recommend the ["Statistical Modeling: The Two Cultures"][1] paper by Leo Breiman in 2001 (cited 515) I know it was covered by the journal club recently and I found it to be really interesting. I've c&p'd the abstract.

Abstract. There are two cultures in
  the use of statistical modeling to
  reach conclusions from data. One
  assumes that the data are generated by
  a given stochastic data model. The
  other uses algorithmic models and
  treats the data mechanism as unknown.
  The statistical community has been
  committed to the almost exclusive use
  of data models. This commitment has
  led to irrelevant theory, questionable
  conclusions, and has kept
  statisticians from working on a large
  range of interesting current problems.
  Algorithmic modeling, both in theory
  and practice, has developed rapidlyin
  fields outside statistics. It can be
  used both on large complex data sets
  and as a more accurate and informative
  alternative to data modeling on
  smaller data sets. If our goal as a
  field is to use data to solve
  problems, then we need to move away
  from exclusive dependence on data
  models and adopt a more diverse set of
  tools.
[1]:
  https://doi.org/10.1214/ss/1009213726 (open access)

A: From a genetic epidemiology perspective, I would now recommend the following series of papers about genome-wide association studies:


*

*Cordell, H.J. and Clayton, D.G. (2005). Genetic association studies. Lancet 366, 1121-1131.

*Cantor, R.M., Lange, K., and Sinsheimer, J.S. (2010). Prioritizing GWAS results: A review of statistical methods and recommendations for their application. The American Journal of Human Genetics 86, 6–22.

*Ioannidis, J.P.A., Thomas, G., Daly, M.J. (2009). Validating, augmenting and refining genome-wide association signals. Nature Reviews Genetics 10, 318-329.

*Balding, D.J. (2006). A tutorial on statistical methods for population association studies. Nature Reviews Genetics 7, 781-791.

*Green, A.E. et al. (2008). Using genetic data in cognitive neuroscience: from growing pains to genuine insights. Nature Reviews Neuroscience 9, 710-720.

*McCarthy, M.I. et al. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics 9, 356-369.

*Psychiatric GWAS Consortium Coordinating Committee (2009). Genomewide Association Studies: History, Rationale, and Prospects for Psychiatric Disorders. American Journal of Psychiatry 166(5), 540-556.

*Sebastiani, P. et al. (2009). Genome-wide association studies and the genetic dissection of complex traits. American Journal of Hematology 84(8), 504-15.

*The Wellcome Trust Case Control Consortium (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661-678.

*The Wellcome Trust Case Control Consortium (2010). Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature 464, 713-720.

A: Jim Berger's review articles: http://www.stat.duke.edu/~berger/papers.html
You might start with Could Fisher, Jeffreys and Neyman have agreed upon testing? 
A: An article with early impact regarding statistical bioinformatics research:
Jelizarow et al. Over-optimism in bioinformatics: an illustration. Bioinformatics, 2010
It makes for an interesting discussion on bias sources, overfitting, and fishing for significance.
A: It's a bit difficult for me to see what paper might be of interest to you, so let me try and suggest the following ones, from the psychometric literature:

Borsboom, D. (2006). The attack of
  the psychometricians.
  Psychometrika, 71, 425-440.

for dressing the scene (Why do we need to use statistical models that better reflect the underlying hypotheses commonly found in psychological research?), and 

Borsboom, D. (2008). Psychometric
  perspectives on diagnostic
  systems. Journal of Clinical
  Psychology, 64, 1089-1108.

for an applied perspective on diagnostic medicine (transition from yes/no assessment as used in the DSM-IV to the "dimensional" approach intended for the DSM-V). A larger review of latent variable models in biomedical research that I like is:

Rabe-Hesketh, S. and Skrondal, A.
  (2008). Classical latent variable
  models for medical research.
  Statistical Methods in Medical Research, 17(1), 5-32.

A: Here are five highly-cited papers from the last 40 years of the Journal of the Royal Statistical Society, Series C: Applied Statistics with a clear application in the title that caught my eye while scanning through the Web of Knowledge search results:


*

*Sheila M. Gore, Stuart J. Pocock and Gillian R. Kerr (1984). Regression Models and Non-Proportional Hazards in the Analysis of Breast Cancer Survival. Vol. 33, No. 2, pp. 176-195. (Cited 100 times) (Free PDF)

*John Haslett and Adrian E. Raftery (1989). Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource. Vol. 38, No. 1 pp. 1-50 (Cited 156 times)

*Stuart G. Coles and Jonathan A. Tawn (1994). Statistical Methods for Multivariate Extremes: An Application to Structural Design. Vol. 43, No. 1, pp. 1-48. (Cited 99 times)

*Nicholas Lange and Scott L. Zeger (1997). Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Vol. 46, No. 1, pp. 1-29. (Cited 94 times)

*James P. Hughes, Peter Guttorp and Stephen P. Charles (1999). A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence. Vol. 48, No. 1, pp. 15-30. (Cited 103 times)

