Quantum percolation in quantum spin Hall antidot systems

We study the influences of antidot-induced bound states on transport properties of two- dimensional quantum spin Hall insulators. The bound statesare found able to induce quantum percolation in the originally insulating bulk. At some critical antidot densities, the quantum spin Hall phase can be completely destroyed due to the maximum quantum percolation. For systems with periodic boundaries, the maximum quantum percolationbetween the bound states creates intermediate extended states in the bulk which is originally gapped and insulating. The antidot in- duced bound states plays the same role as the magnetic field inthe quantum Hall effect, both makes electrons go into cyclotron motions. We also draw an analogy between the quantum percolation phenomena in this system and that in the network models of quantum Hall effect

Nonparametric inference procedure for percentiles of the random effects distribution in meta-analysis

To investigate whether treating cancer patients with erythropoiesis-stimulating agents (ESAs) would increase the mortality risk, Bennett et al. [Journal of the American Medical Association 299 (2008) 914--924] conducted a meta-analysis with the data from 52 phase III trials comparing ESAs with placebo or standard of care. With a standard parametric random effects modeling approach, the study concluded that ESA administration was significantly associated with increased average mortality risk. In this article we present a simple nonparametric inference procedure for the distribution of the random effects. We re-analyzed the ESA mortality data with the new method. Our results about the center of the random effects distribution were markedly different from those reported by Bennett et al. Moreover, our procedure, which estimates the distribution of the random effects, as opposed to just a simple population average, suggests that the ESA may be beneficial to mortality for approximately a quarter of the study populations. This new meta-analysis technique can be implemented with study-level summary statistics. In contrast to existing methods for parametric random effects models, the validity of our proposal does not require the number of studies involved to be large. From the results of an extensive numerical study, we find that the new procedure performs well even with moderate individual study sample sizes.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS280 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org