Time for our third annual Eco-Stats Paper of the Year awards. Basically everyone in our group nominates the paper they were most impressed by this year, across the ecology and statistics literatures (although ideally somewhere between the two). Then we have a vote for a winner. A bit of a stats focus this year as it turns out, perhaps reflecting where our thinking has been recently. Here is our shortlist:
Kitzes and Harte Beyond the species–area relationship: improving macroecological extinction estimates, Methods in Ecology and Evolution. There is something of a disconnect between broad macroecological methods (like species-area relationships) and methods for modelling individual species (SDMs etc), and this paper tries to bridge that gap a little with some nice ideas.
Godoy, Kraft and Levine Phylogenetic relatedness and the determinants of competitive outcomes, Ecology Letters. An interesting mix of empirical work and theory, meticulously collecting data on vital rates in a competition experiment to parameterise a fancy Chesson mathematical model for competition and look at what this implied concerning the pairwise competitive interactions between a set of 18 Californian grassland species. Results question the widely held expectation that more distantly related species can more readily coexist.
Delaigle Nonparametric Kernel Methods with Errors-in-Variables: Constructing Estimators, Computing them, and Avoiding Common Mistakes, Australian and New Zealand Journal of Statistics. An insightful review of ways to handle measurement error in kernel estimation, with a section on common mistakes seen in the literature. Aurore Delaigle (Melbourne) is one to watch on the Australian Statistics scene, a Peter Hall protege and winner of the 2012 Moran Medal.
Viladomat, .., Hastie Assessing the significance of global and local correlations under spatial autocorrelation: A nonparametric approach, Biometrics. Not the first Stanford Stats entry, and not the last either! A permutation test for association between two spatial variables, which works by smoothing and scaling values in the permuted variable in such a way that it preserves autocorrelation. An original idea working away at the problematic area of design-based inference for spatial data.
And the two joint winners this year:
Kleiner et al A scalable bootstrap for massive data, JRSSB.
Bootstrapping target quantities in large datasets by breaking the data
into groups and boostrapping the summary statistics for each group.
This idea makes bootstrapping doable for large datasets, if you have the
right sort of statistic.
Lockhart, .., Tibshirani A significance test for the lasso, Annals of Statistics. The LASSO is a big deal these days but a sticking point has always been inference about coefficients. This paper proposes an amazingly simple significance test for coefficients entering the model. This is destined to become a citation classic.
Feel free to share your own opinions on the highlights of 2014!