Volumes of logistic regression models and their use for model selection.

TL;DR: In this article, the authors give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.

TL;DR: The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to within asymptotically vanishing terms.

TL;DR: A sharper code length is obtained as the stochastic complexity and the associated universal process are derived for a class of parametric processes by taking into account the Fisher information and removing an inherent redundancy in earlier two-part codes.