区间线性规划的标准型及其最优值区间

定义了区间线性规划的标准型. 研究求解标准型区间线性规划的最好最优值和最差最 优值,从而确定其最优值区间. 分别对区间目标函数、区间不等式约束和区间等式约束作了讨 论. 基于此分别构造了求解区间线性规划最好最优值和最差最优值的确定型线性规划模型. 最 后给出一个算例并对一些特殊情况作了补充说明     

企业文化系统及其塑造研究

在心理契约与企业文化比较研究的基础上,以心理契约为基础,给出了企业文化的定 义,较系统地研究了企业文化理论;构建了企业文化系统模型,从塑造的角度解析了其维度构 成;并应用绩效报酬激励机制,提出建设企业强文化的方法. 最后,运用案例进行了实证分析     

组合拍卖竞胜标确定问题的混沌搜索算法

组合拍卖能够提高拍卖的效率,还能降低竞标人的风险. 但竞胜标确定问题是一个NP 难题. 在分析该问题特性的基础上,设计了一种嵌入优先适合启发式规则的混沌搜索算法. 与 传统算法相比,该算法具有实现方便,寻优效果好的优点. 实例计算结果表明了算法在解决该 问题的有效性和广阔的应用前景.     

大规模定制模式下供应链计划调度优化分析

大规模定制模式下供应链生产计划调度问题是一个典型的随机需求与随机资源约束的 多目标动态优化问题. 在对该问题特征翔实描述,分析所总结的理论研究成果基础上,提出了 完整的随机多目标动态优化数学模型. 通过实例简要分析了优化目标的成熟性及模型的可行 性. 最后,指出了较为重要的动态优化调度过程的实现,并进行了实践应用过程的验证与说明     

非对称信息条件下实物期权最优投资问题研究

描述了实物期权投资者和经营者价值函数,分析了不同信息条件下实物期权的最优投 资决策. 在非对称信息条件下,实物期权经营者对于项目价值信息隐匿,这是一个具有逆向选 择的委托代理问题. 设计了以实物期权投资者利润数学期望最大为目标函数,以投资和数量折 扣作为状态方程的最优控制问题. 应用极大值原理推导了实物期权最优投资和数量折扣的求 解方案. 最后,进行了实物期权最优投资的仿真实验,验证了实物期权在项目投资问题上的分 析结果.     

交易量和交易量驱动的股价动力学分析方法

近20 年来, 理论界开始更加注重交易量在研究中的重要作用, 特别是从信息的角度出发, 研究者相信交易量能提供独立于股票价格之外的信息. 但是如何将交易量合适地融入价格序列中仍然是一个争论的话题. 本文系统地回顾了国内外有关交易量的研究现状, 指出由于累计交易量是时间的单调增函数, 与时间相比, 交易量不仅包含有时间价值因素, 而且还包含了交易成本等信息价值因素. 随后本文提出了交易量驱动的价格变化的研究思想. 在此基础上给出了基于交易量进程的股价动力学分析方法以及两种构造交易量进程的股价序列方法, 指出在日历时间假设下的时间序列和在交易时间假设下的时间序列是交易量进程假设的特例. 通过随机选择18 个股票的实证研究初步表明, 在更低的阶数或更少的参数情况下, 本模型具有更高的拟合精度和解释能力.     

基于流程图及过程代数的流程表达方法

提出一种新的用于BPR 的业务流程表达方法. 首先用基于活动属性的流程图来描述业 务流程; 再利用图论的矩阵分析方法描述流程图; 然后结合过程代数的方法, 由流程图的矩阵 推导出一个能全面反映流程情况的代数表达式——过程代数式; 最后通过对过程代数式的分 析, 提出流程的评价指标.     

Modeling longitudinal binomial responses: implications from two dueling paradigms

The generalized estimating equations (GEEs) and generalized linear mixed-effects model (GLMM) are the two most popular paradigms to extend models for cross-sectional data to a longitudinal setting. Although the two approaches yield well-interpreted models for continuous outcomes, it is quite a different story when applied to binomial responses. We discuss major modeling differences between the GEE- and GLMM-derived models by presenting new results regarding the model-driven differences. Our results show that GLMM induces some artifacts in the marginal models at assessment times, making it inappropriate when applied to such responses from real study data. The different interpretations of parameters resulting from the conceptual difference between the two modeling approaches also carry quite significant implications and ramifications with respect to data and power analyses. Although a special case involving a scale difference in parameters between GEE and GLMM has been noted in the literature, its implications in real data analysis has not been thoroughly addressed. Further, this special case has a very limited covariate structure and does not apply to most real studies, especially multi-center clinical trials. The new results presented fill a substantial gap in the literature regarding the model-driven differences between the two dueling paradigms.     

Robust penalized logistic regression with truncated loss functions

The penalized logistic regression (PLR) is a powerful statistical tool for classification. It has been commonly used in many practical problems. Despite its success, since the loss function of the PLR is unbounded, resulting classifiers can be sensitive to outliers. To build more robust classifiers, we propose the robust PLR (RPLR) which uses truncated logistic loss functions, and suggest three schemes to estimate conditional class probabilities. Connections of the RPLR with some other existing work on robust logistic regression have been discussed. Our theoretical results indicate that the RPLR is Fisher consistent and more robust to outliers. Moreover, we develop estimated generalized approximate cross validation (EGACV) for the tuning parameter selection. Through numerical examples, we demonstrate that truncating the loss function indeed yields better performance in terms of classification accuracy and class probability estimation.     

Self‐consistent estimation of mean response functions and their derivatives

Many methods have been developed for the nonparametric estimation of a mean response function, but most of these methods do not lend themselves to simultaneous estimation of the mean response function and its derivatives. Recovering derivatives is important for analyzing human growth data, studying physical systems described by differential equations, and characterizing nanoparticles from scattering data. In this article the authors propose a new compound estimator that synthesizes information from numerous pointwise estimators indexed by a discrete set. Unlike spline and kernel smooths, the compound estimator is infinitely differentiable; unlike local regression smooths, the compound estimator is self‐consistent in that its derivatives estimate the derivatives of the mean response function. The authors show that the compound estimator and its derivatives can attain essentially optimal convergence rates in consistency. The authors also provide a filtration and extrapolation enhancement for finite samples, and the authors assess the empirical performance of the compound estimator and its derivatives via a simulation study and an application to real data. The Canadian Journal of Statistics 39: 280–299; 2011 © 2011 Statistical Society of Canada