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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.

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    $\begingroup$ Do you have some general interests that you'd like to list? That might help guide suggestions. Applications of statistics have become pretty pervasive in a remarkably broad array of fields. $\endgroup$
    – cardinal
    Commented Apr 9, 2011 at 1:58
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    $\begingroup$ @cardinal, nope, no particular interests -- the purpose was to branch out from the stuff I typically read, so I'm trying not to limit any answers. (This does maybe make the question a bit too broad, but I guess I'm looking for people's personal "best of" lists.) $\endgroup$
    – raegtin
    Commented Apr 11, 2011 at 21:40
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    $\begingroup$ A classic must-read, especially because all the probability models introduced are motivated by "physical" reasoning about the problem, rather than pulled out of a hat, is: F. Mosteller, D. L. Wallace (1963): Inference in an authorship problem: A comparative study of discrimination methods applied to the authorship of the disputed Federalist papers, J. Am. Stat. Assoc. 58 (302), pp. 275–309. Also at this link. $\endgroup$
    – pglpm
    Commented Mar 2, 2019 at 12:38

6 Answers 6

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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.

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  • $\begingroup$ @ chl (+1) those Borsboom papers were wonderful, they really broadened my thinking about measurement $\endgroup$
    – richiemorrisroe
    Commented Apr 9, 2011 at 13:13
  • $\begingroup$ +1, I enjoy Borsboom as well. For those interested in The Attack article I think would also be interested in "The Concept of Validity", rhowell.ba.ttu.edu/borsboomValidity2004.pdf . Although it is a little more verbose so it is not as easy to follow as the Attack article. $\endgroup$
    – Andy W
    Commented Apr 10, 2011 at 13:08
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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:

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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)

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

  1. Cordell, H.J. and Clayton, D.G. (2005). Genetic association studies. Lancet 366, 1121-1131.
  2. 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.
  3. Ioannidis, J.P.A., Thomas, G., Daly, M.J. (2009). Validating, augmenting and refining genome-wide association signals. Nature Reviews Genetics 10, 318-329.
  4. Balding, D.J. (2006). A tutorial on statistical methods for population association studies. Nature Reviews Genetics 7, 781-791.
  5. Green, A.E. et al. (2008). Using genetic data in cognitive neuroscience: from growing pains to genuine insights. Nature Reviews Neuroscience 9, 710-720.
  6. McCarthy, M.I. et al. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics 9, 356-369.
  7. Psychiatric GWAS Consortium Coordinating Committee (2009). Genomewide Association Studies: History, Rationale, and Prospects for Psychiatric Disorders. American Journal of Psychiatry 166(5), 540-556.
  8. Sebastiani, P. et al. (2009). Genome-wide association studies and the genetic dissection of complex traits. American Journal of Hematology 84(8), 504-15.
  9. 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.
  10. 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.
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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?

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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.

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