On the Existence of General Factors in Regular Graphs

Let $G$ be a graph, and $H\colon V(G)\to 2^\mathbb{N}$ a set function associated with $G$. A spanning subgraph $F$ of $G$ is called an $H$-factor if the degree of any vertex $v$ in $F$ belongs to the set $H(v)$. This paper contains two results on the existence of $H$-factors in regular graphs. First, we construct an $r$-regular graph without some given $H^*$-factor. In particular, this gives a negative answer to a problem recently posed by Akbari and Kano. Second, by using Lov\'asz's characterization theorem on the existence of $(g, f)$-factors, we find a sharp condition for the existence of general $H$-factors in $\{r, r+1\}$-graphs, in terms of the maximum and minimum of $H$. The result reduces to Thomassen's theorem for the case that $H(v)$ consists of the same two consecutive integers for all vertices $v$, and to Tutte's theorem if the graph is regular in addition.Comment: 10 page

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

Study on the spectrum of the injected relativistic protons

About 10TeV gamma-ray emission within 10 pc region from the Galactic Center had been reported by 4 independent groups. Considering that this TeV gamma-ray emission is produced via a hadronic model, and the relativistic protons came from the tidal disruption of stars by massive black holes, we investigate the spectral nature of the injected relativistic protons required by the hadronic model. The calculation was carried on the tidal disruption of the different types of stars and the different propagation mechanisms of protons in the interstellar medium. Compared with the observation data from HESS, we find for the best fitting that the power-law index of the spectrum of the injected protons is about -1.9, when a red giant star is tidally disrupted, and the effective confinement of protons diffusion mechanism is adopted.Comment: 2 pages, IAU Symposium 25