Solar models and electron screening

We investigate the sensitivity of the solar model to changes in the nuclear reaction screening factors. We show that the sound speed profile as determined by helioseismology certainly rules out changes in the screening factors exceeding more than 10%. A slightly improved solar model could be obtained by enhancing screening by about 5% over the Salpeter value. We also discuss how envelope properties of the Sun depend on screening, too. We conclude that the solar model can be used to help settling the on-going dispute about the ``correct'' screening factors.Comment: accepted for publication by Astron. Astrophy

Screening and collective modes in gapped bilayer graphene

We study the static and dynamic screening of gapped bilayer graphene. Unlike previous works we use the 4-band model instead of the simplified 2-band model. We find that there are important qualitative differences between the dielectric screening function obtained using the 2-band and that obtained using the 4-band model. In particular within the 4-band model in the presence of a band-gap the static screening exhibits Kohn anomalies that are absent within the 2-band model. Moreover, using the 4-band model, we are able to examine the effect of trigonal warping (absent in the 2-band model) on the screening properties of bilayer graphene. We also find that the plasmon modes have qualitatively different character in the 4-band model compared to 2-band results.Comment: 4 pages, 5 figures. Published versio

The credibility of health economic models for health policy decision-making: the case of population screening for abdominal aortic aneurysm

<i>Objectives</i>: To review health economic models of population screening for abdominal aortic aneurysm (AAA) among elderly males and assess their credibility for informing decision-making. <i>Methods</i>: A literature review identified health economic models of ultrasound screening for AAA. For each model focussing on population screening in elderly males, model structure and input parameter values were critically appraised using published good practice guidelines for decision analytic models. <i>Results</i>: Twelve models published between 1989 and 2003 were identified. Converting costs to a common currency and base year, substantial variability in cost-effectiveness results were revealed. Appraisals carried out for the nine models focusing on population screening showed differences in their complexity, with the simpler models generating results most favourable to screening. Eight of the nine models incorporated two or more simplifying structural assumptions favouring screening; uncertainty surrounding these assumptions was not investigated by any model. Quality assessments on a small number of parameters revealed input values varied between models, methods used to identify and incorporate input data were often not described, and few sensitivity analyses were reported. <i>Conclusions</i>: Large variation exists in the cost-effectiveness results generated by AAA screening models. The substantial number of factors potentially contributing to such disparities means that reconciliation of model results is impossible. In addition, poor reporting of methods makes it difficult to identify the most plausible and thus most useful model of those developed

Doubly Robust Sure Screening for Elliptical Copula Regression Model

Regression analysis has always been a hot research topic in statistics. We propose a very flexible semi-parametric regression model called Elliptical Copula Regression (ECR) model, which covers a large class of linear and nonlinear regression models such as additive regression model,single index model. Besides, ECR model can capture the heavy-tail characteristic and tail dependence between variables, thus it could be widely applied in many areas such as econometrics and finance. In this paper we mainly focus on the feature screening problem for ECR model in ultra-high dimensional setting. We propose a doubly robust sure screening procedure for ECR model, in which two types of correlation coefficient are involved: Kendall tau correlation and Canonical correlation. Theoretical analysis shows that the procedure enjoys sure screening property, i.e., with probability tending to 1, the screening procedure selects out all important variables and substantially reduces the dimensionality to a moderate size against the sample size. Thorough numerical studies are conducted to illustrate its advantage over existing sure independence screening methods and thus it can be used as a safe replacement of the existing procedures in practice. At last, the proposed procedure is applied on a gene-expression real data set to show its empirical usefulness.Comment: 22 pages, 2 figure

Better subset regression

To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model fitting) than all that do not in a general asymptotic setting. This indicates that, for screening important variables, we could follow a "better fitting, better screening" rule, i.e., pick a "better" subset that has better model fitting. To seek such a better subset, we consider the optimization problem associated with best subset regression. An EM algorithm, called orthogonalizing subset screening, and its accelerating version are proposed for searching for the best subset. Although the two algorithms cannot guarantee that a subset they yield is the best, their monotonicity property makes the subset have better model fitting than initial subsets generated by popular screening methods, and thus the subset can have better screening performance asymptotically. Simulation results show that our methods are very competitive in high dimensional variable screening even for finite sample sizes.Comment: 24 pages, 1 figur