A Simple Information Criterion for Variable Selection in High-Dimensional Regression.

High-dimensional regression problems, for example with genomic or drug exposure data, typically involve automated selection of a sparse set of regressors. Penalized regression methods like the LASSO can deliver a family of candidate sparse models. To select one, there are criteria balancing log-likelihood and model size, the most common being AIC and BIC.

A general method for estimating seed dormancy and colonisation in annual plants from the observation of existing flora.

In plant ecology, characterising colonisation and extinction in plant metapopulations is challenging due to the non-detectable seed bank that allows plants to emerge after several years of absence. In this study, we used a Hidden Markov Model to characterise seed dormancy, colonisation and germination solely from the presence-absence of standing flora. Applying the model to data from a long-term survey of 38 annual weeds across France, we identified three homogeneous functional groups: (1) species persisting preferentially through spatial colonisation, (2) species persisting preferentially through seed dormancy and (3) a mix of both strategies.