There’s lots of overlap but I put each paper into only one category. Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later. So you can can think of this list as representing roughly two years’ work.

Political science:

- [2014] The twentieth-century reversal: How did the Republican states switch to the Democrats and vice versa? {\em Statistics and Public Policy}. (Andrew Gelman)
- [2013] Hierarchical models for estimating state and demographic trends in U.S. death penalty public opinion. {\em Journal of the Royal Statistical Society A}. (Kenneth Shirley and Andrew Gelman)
- [2013] Deep interactions with MRP: Election turnout and voting patterns among small electoral subgroups. {\em American Journal of Political Science}. (Yair Ghitza and Andrew Gelman)
- [2013] Charles Murray’s {\em Coming Apart} and the measurement of social and political divisions. {\em Statistics, Politics and Policy}. (Andrew Gelman)
- Forecasting elections with non-representative polls. (Wei Wang, David Rothschild, Sharad Goel, Andrew Gelman)

Survey methods:

- [2013] A practical guide to measuring social structure using indirectly observed network data. {\em Journal of Statistical Theory and Practice}. (Tyler McCormick, Amal Moussa, Johannes Ruf, Thomas DiPrete, Andrew Gelman, Julien Teitler, and Tian Zheng)
- Multiple imputation for continuous and categorical data: Comparing joint and conditional approaches. (Jonathan Kropko, Ben Goodrich, Andrew Gelman, and Jennifer Hill)
- Bayesian nonparametric weighted sampling inference. (Yajuan Si, Natesh Pillai, and Andrew Gelman)
- Weighting adjustments for panel nonresponse. (Qixuan Chen, Andrew Gelman, Melissa Tracy, Fran H. Norris, and Sandro Galea)

Statistical graphics:

- [2013] Infovis and statistical graphics: Different goals, different looks (with discussion). {\em Journal of Computational and Graphical Statistics}. (Andrew Gelman and Antony Unwin)

Tradeoffs in information graphics (rejoinder to discussion). (Andrew Gelman and Antony Unwin)

Bayesian methods:

- [2013] A nondegenerate estimator for hierarchical variance parameters via penalized likelihood estimation. {\em Psychometrika} {\bf 78}, 685–709. (Yeojin Chung, Sophia Rabe-Hesketh, Andrew Gelman, Jingchen Liu, and Vincent Dorie)
- [2013] Two simple examples for understanding posterior p-values whose distributions are far from unform. {\em Electronic Journal of Statistics}. (Andrew Gelman)
- [2013] Understanding predictive information criteria for Bayesian models. {\em Statistics and Computing}. (Andrew Gelman, Jessica Hwang, and Aki Vehtari)
- [2013] Nonparametric models can be checked. {\em Bayesian Analysis}. (Andrew Gelman)
- [2014] How Bayesian analysis cracked the red-state, blue-state problem. {\em Statistical Science}. (Andrew Gelman)
- [2013] Does quantum uncertainty have a place in everyday applied statistics? {\em Behavioral and Brain Sciences} {\bf 36}, 285. (Andrew Gelman and Michael Betancourt)
- Revised evidence for statistical standards. (Andrew Gelman and Christian Robert)
- A problem with the use of cross-validation for selecting among multilevel models. (Wei Wang and Andrew Gelman)
- Weakly informative prior for point estimation of covariance matrices in hierarchical models. (Yeojin Chung, Andrew Gelman, Sophia Rabe-Hesketh, Jun Liu, and Vincent Dorie)

Causal inference:

- Why ask why? Forward causal inference and reverse causal questions. (Andrew Gelman and Guido Imbens)

Statistical computing:

- [2014] On the stationary distribution of iterative imputations. {\em Biometrika}. (Jingchen Liu, Andrew Gelman, Jennifer Hill, Yu-Sung Su, and Jonathan Kropko)
- [2013] The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. {\em Journal of Machine Learning Research}. (Matt Hoffman and Andrew Gelman)
- Simulation-efficient shortest probability intervals. (Ying Liu, Andrew Gelman, and Tian Zheng)

Ethics:

- [2014] The AAA tranche of subprime science. {\em Chance}. (Andrew Gelman and Eric Loken)
- [2013] Is it possible to be an ethicist without being mean to people? {\em Chance} {\bf 26} (4). (Andrew Gelman)
- [2013] It’s too hard to publish criticisms and obtain data for replication. {\em Chance} {\bf 26} (3), 49–52. (Andrew Gelman)
- [2013] To throw away data: Plagiarism as a statistical crime. {\em American Scientist} {\bf 101}, 168–171. (Andrew Gelman and Thomas Basboll)
- [2013] They’d rather be rigorous than right. {\em Chance} {\bf 26} (2), 45–49. (Andrew Gelman)
- [2013] The war on data. {\em Chance} {\bf 26} (1). (Andrew Gelman and Mark Palko)

History and philosophy of statistics:

- [2013] To throw away data: Plagiarism as a statistical crime. {\em American Scientist} {\bf 101}, 168–171. (Andrew Gelman and Thomas Basboll)
- [2013] Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin. {\em Statistics \& Risk Modeling} {\bf 30}, 1001–1016. (Andrew Gelman, Christian Robert, and Judith Rousseau)
- [2013] How do we choose our default methods? For the Committee of Presidents of Statistical Societies (COPSS) 50th anniversary volume. (Andrew Gelman)
- [2013] “Not only defended but also applied”: The perceived absurdity of Bayesian inference (with discussion). {\em American Statistician}. (Andrew Gelman and Christian Robert)

The anti-Bayesian moment and its passing (rejoinder to discussion). (Andrew Gelman and Christian Robert) - [2013] Philosophy and the practice of Bayesian statistics (with discussion). {\em British Journal of Mathematical and Statistical Psychology} {\bf 66}, 8–18. (Andrew Gelman and Cosma Shalizi) [2013] Rejoinder to discussion. (Andrew Gelman and Cosma Shalizi)
- When do stories work? Evidence and illustration in the social sciences. (Andrew Gelman and Thomas Basboll)

Replication and scientific publication:

- [2014] The connection between varying treatment effects and the crisis of unreplicable research: A Bayesian perspective. {\em Journal of Management}. (Andrew Gelman)
- [2013] Convincing evidence. For a volume on theoretical or methodological research on authorship, functional roles, reputation, and credibility on social media, ed.\ Sorin Matei and Elisa Bertino. (Andrew Gelman and Keith O’Rourke)
- [2013] In praise of the referee. {\em International Society for Bayesian Analysis Bulletin} {\bf 20} (1), 13–19. (Nicolas Chopin, Andrew Gelman, Kerrie Mengersen, and Christian Robert)
- [2013] Difficulties in making inferences about scientific truth from distributions of published p-values. {\em Biostatistics}. (Andrew Gelman and Keith O’Rourke)
- [2013] Preregistration of studies and mock reports. {\em Political Analysis} {\bf 21}, 40–41. (Andrew Gelman)
- The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time (Andrew Gelman and Eric Loken)
- Beyond power calculations to a broader design analysis, prospective or retrospective, using external information. (Andrew Gelman and John Carlin)

Other:

- [2013] Rates and correlates of HIV and STI infection among homeless women. {\em AIDS and Behavior} {\bf 17}, 856-864.(Carol L. M. Caton, Nabila El-Bassel, Andrew Gelman, Susan Barrow, Daniel Herman, Eustace Hsu, Ana Z. Tochterman, Karen Johnson, and Alan Felix)
- Centralized analysis of local data, with dollars and lives on the line: Lessons from the home radon experience. (Phillip N. Price and Andrew Gelman)

I love (almost) all of this work so I hate to see various important projects buried in the middle of the list. I just hope the different sort of papers reach their appropriate audiences.

Also, BDA3 (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin) and Stan (with Bob Carpenter, Matt Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Lee, and Allen Riddell).

Thanks again to all my collaborators and discussants of all sorts (including blog commenters, and even including trolls and people who send me rude emails, as these too can stimulate useful thoughts and can reveal important gaps in communication), as well as our sponsors for their financial support of this work.

Andrew,

Firstly, congratulations – a magnificent tally.

Secondly, I worry you are not getting enough sleep! Perhaps a project for 2014 could be relating your sleep to your outputs. Or, in the absence of informed consent, a censored exposure variable from the timestamps on your blog posts! (9 am is respectable)

With regard to Kropko et. al./MI for continuous and categorical data, we’ve had some luck joint modeling with NP Bayes (http://stat.duke.edu/~jsm38/preprints/hcmm.pdf). We’re able to pick up complex relationships even with a default model specification. We’re working on methods that incorporate constraints as well (structural zeros, linear constraints, semicontinuous variables, etc).

One of the things we astronomers often believe about statisticians is that they only publish one paper every year or two. I guess we are wrong about that… And now I don’t feel so bad about sending all the rude emails. Here’s to 2014!

All I can say is “Wow!” If that’s a typical (2) year(s) for you, I’m more than impressed. Actually, I wonder how you find time for the blog…or to sleep…

I feel bad for people who are trying to get a job at the same level. “Publish or perish”, they say, well… just how much does anyone have to publish?