Bayesian model selection in high-dimensional settings

Standard assumptions incorporated into Bayesian model selection procedures result in procedures that are not competitive with commonly used penalized likelihood methods. We propose modifications of these methods by imposing nonlocal prior densities on model parameters. We show that the resulting model selection procedures are consistent in linear model settings when the number of possible covariates p is bounded by the number of observations n, a property that has not been extended to other model selection procedures. In addition to consistently identifying the true model, the proposed procedures provide accurate estimates of the posterior probability that each identified model is correct. Through simulation studies, we demonstrate that these model selection procedures perform as well or better than commonly used penalized likelihood methods in a range of simulation settings. Proofs of the primary theorems are provided in the Supplementary Material that is available online

Dry Sliding Wear Characteristics of Gravity Die-Cast Magnesium Alloys

The paper deals with the wear behavior of conventional cast Mg-Sn-based alloys. The alloys were studied through pin-on-disk wear test under four different loading conditions; namely, 9.8, 19.6, 29.4, and 39.2 N. The study highlights the cumulative wear loss, volumetric wear loss, dry sliding wear rate, and coefficient of friction of the alloys. The volumetric wear increased with increasing applied load. The wear mechanism was studied with scanning electron microscope. The wear occurs mainly by plowing mechanism and also by delamination. During wear, extensive plastic deformation and work hardening occurred. Microstructural analysis has been carried out for all the alloys at different loading conditions. (C) The Minerals, Metals & Materials Society and ASM International 201