General Sparse Boosting: Improving Feature Selection of L2 Boosting by Correlation-Based Penalty Family

In high-dimensional setting, componentwise L₂boosting has been used to construct sparse model that performs well, but it tends to select many ineffective variables. Several sparse boosting methods, such as, SparseL₂Boosting and Twin Boosting, have been proposed to improve the variable selection of L₂boosting algorithm. In this article, we propose a new general sparse boosting method (GSBoosting). The relations are established between GSBoosting and other well known regularized variable selection methods in the orthogonal linear model, such as adaptive Lasso, hard thresholds, etc. Simulation results show that GSBoosting has good performance in both prediction and variable selection.     

网络环境下高师图书馆的文献资源建设

本文阐述了高师图书馆在网络环境下文献资源建设的新情况、新问题 ,论述了新形势下高师图书馆文献资源建设和信息资源建设的方针政策以及解决这些问题所应采取的具体措施     

区域经济发展模式的特点与选择

我国经济理论界和实际工作,根据国情特点,联系各地不平衡发展的现状,先后提出了梯度推进发展,点轴渐进发展,区域经济协调发展,城市圈域经济发展和网状交织发展等区域经济发展模式。它们有不同的特色和优势,但也存在某些不足。从长远看,比较起来,网状交织发展模式更有利于提高区域经济的运行质量,宜于进入工业化中后期阶段的地区采用。     

当代社会老年人赡养支持网研究

面对老龄化社会的到来,越来越多的人提出走"养老保障社会化"之路.但目前这种建议和对策实施的效果如何?延续了几千年的家庭养老方式是否被社会养老模式所代替?就此进行了深入的探讨和回答.     

略论财务会计的前沿态势

网络技术的发展,为会计领域带来了新的活力和契机。它不仅使会计本身概念框架发生前所未有的变化,也将引发对现行会计规则的挑战,对传统会计理论与方法的整合。会计信息化的本质是会计与现代信息技术相融合的发展过程,它必将成为会计未来的发展趋势。     

弹性生态观视域下海洋渔业生态补偿政策设计

由于过度捕捞和环境污染,海洋渔业发展面临着资源衰竭、环境受损、生态失衡等一系列问题,海洋渔业生态系统承受着前所未有的压力,如若继续恶化,后果不言而喻。然而,现行的渔业生态保护与管理方式不是可持续发展的,甚至是背道而驰的。海洋渔业可持续发展需要创新的思维。弹性思维提供了理解周围世界和管理自然资源的一种不同方式,提倡用弹性的观念去看待事物的变化,是一种旨在增加系统可持续性的资源管理新方法。弹性思维及其所蕴含的弹性生态观既可以成为海洋渔业可持续发展的有效途径,又可以为海洋渔业生态补偿提供理论支持。     

Sample Size Calculation and Blinded Sample Size Recalculation in Clinical Trials Where the Treatment Effect is Measured by the Relative Risk

In clinical trials with binary endpoints, the required sample size does not depend only on the specified type I error rate, the desired power and the treatment effect but also on the overall event rate which, however, is usually uncertain. The internal pilot study design has been proposed to overcome this difficulty. Here, nuisance parameters required for sample size calculation are re-estimated during the ongoing trial and the sample size is recalculated accordingly. We performed extensive simulation studies to investigate the characteristics of the internal pilot study design for two-group superiority trials where the treatment effect is captured by the relative risk. As the performance of the sample size recalculation procedure crucially depends on the accuracy of the applied sample size formula, we firstly explored the precision of three approximate sample size formulae proposed in the literature for this situation. It turned out that the unequal variance asymptotic normal formula outperforms the other two, especially in case of unbalanced sample size allocation. Using this formula for sample size recalculation in the internal pilot study design assures that the desired power is achieved even if the overall rate is mis-specified in the planning phase. The maximum inflation of the type I error rate observed for the internal pilot study design is small and lies below the maximum excess that occurred for the fixed sample size design.     

e-He弹性散射的相移计算

用与半经典交换势（ＳＣＥ）不同的三种交换势计算了低能ｅ－Ｈｅ弹性散射的相移，结果表明，这三种势均在不同程度上改善了ＳＣＥ势的结果．     

An adaptive sampling scheme guided by BART—with an application to predict processor performance

The evaluation of new processor designs is an important issue in electrical and computer engineering. Architects use simulations to evaluate designs and to understand trade‐offs and interactions among design parameters. However, due to the lengthy simulation time and limited resources, it is often practically impossible to simulate a full factorial design space. Effective sampling methods and predictive models are required. In this paper, the authors propose an automated performance predictive approach which employs an adaptive sampling scheme that interactively works with the predictive model to select samples for simulation. These samples are then used to build Bayesian additive regression trees, which in turn are used to predict the whole design space. Both real data analysis and simulation studies show that the method is effective in that, though sampling at very few design points, it generates highly accurate predictions on the unsampled points. Furthermore, the proposed model provides quantitative interpretation tools with which investigators can efficiently tune design parameters in order to improve processor performance. The Canadian Journal of Statistics 38: 136–152; 2010 © 2010 Statistical Society of Canada     

Using adaptive weighted least squares to reduce the lengths of confidence intervals

The author proposes an adaptive method which produces confidence intervals that are often narrower than those obtained by the traditional procedures. The proposed methods use both a weighted least squares approach to reduce the length of the confidence interval and a permutation technique to insure that its coverage probability is near the nominal level. The author reports simulations comparing the adaptive intervals to the traditional ones for the difference between two population means, for the slope in a simple linear regression, and for the slope in a multiple linear regression having two correlated exogenous variables. He is led to recommend adaptive intervals for sample sizes superior to 40 when the error distribution is not known to be Gaussian.