On multivariate confidence regions and simultaneous confidence limits for ratios

Convenient general linear model computational procedures are presented for constructing multivariate confidence regions and simultaneous confidence limits for ratios of linear combinations of the parameters. The practical consequence is that a single general linear model computer program, capable of validating the underlying model and estimating the parameters, can (after slight modification) also construct the confidence regions, and even determine their precise analytic form (ellipsoid, hyperboloid, etc.). The text is deliberately factual while the appendices extend and help clarify earlier work by Henry Scheffe. As an example, a confidence ellipse and simultaneous confidence limits are constructed for several relative potencies in a classical multiple parallel line bioassay.     

Simple and flexible Bayesian inferences for standardized regression coefficients

ABSTRACT

In statistical practice, inferences on standardized regression coefficients are often required, but complicated by the fact that they are nonlinear functions of the parameters, and thus standard textbook results are simply wrong. Within the frequentist domain, asymptotic delta methods can be used to construct confidence intervals of the standardized coefficients with proper coverage probabilities. Alternatively, Bayesian methods solve similar and other inferential problems by simulating data from the posterior distribution of the coefficients. In this paper, we present Bayesian procedures that provide comprehensive solutions for inferences on the standardized coefficients. Simple computing algorithms are developed to generate posterior samples with no autocorrelation and based on both noninformative improper and informative proper prior distributions. Simulation studies show that Bayesian credible intervals constructed by our approaches have comparable and even better statistical properties than their frequentist counterparts, particularly in the presence of collinearity. In addition, our approaches solve some meaningful inferential problems that are difficult if not impossible from the frequentist standpoint, including identifying joint rankings of multiple standardized coefficients and making optimal decisions concerning their sizes and comparisons. We illustrate applications of our approaches through examples and make sample R functions available for implementing our proposed methods.     

多国股票市场的高频波动相关性研究

本文通过建立包含马尔科夫机制转换结构的MS-MHAR-DCC模型，并选取世界上比较发达的国家和地区股票市场的高频日内交易数据为样本，对多个股票市场波动相关性进行研究。通过引入包含马尔科夫结构的外部随机矩阵，本文识别出金融市场波动相关的截断时期，正态分布设定下相比在t分布设定下识别的截断时期更多且持续时间更长。在模型的截断时期内，多个股票市场的波动相关结构主要受到正向冲击，即在截断时期内的波动相关性大于平常状态的波动相关性。本文还发现，相同地域的股票市场间的动态波动相关性在大部分时期内表现为较强的正相关；美国股票市场和其余5个国家股票市场波动的动态相关性在大部分时期都表现为较强的正相关，表明美国作为全球巨头在世界金融市场波动的引导作用。     

Post hoc false positive control for structured hypotheses

In a high-dimensional multiple testing framework, we present new confidence bounds on the false positives contained in subsets S of selected null hypotheses. These bounds are post hoc in the sense that the coverage probability holds simultaneously over all S, possibly chosen depending on the data. This article focuses on the common case of structured null hypotheses, for example, along a tree, a hierarchy, or geometrically (spatially or temporally). Following recent advances in post hoc inference, we build confidence bounds for some prespecified forest-structured subsets and deduce a bound for any subset S by interpolation. The proposed bounds are shown to improve substantially previous ones when the signal is locally structured. Our findings are supported both by theoretical results and numerical experiments. Moreover, our bounds can be obtained by an algorithm (with complexity bilinear in the sizes of the reference hierarchy and of the selected subset) that is implemented in the open-source R package sansSouci available from https://github.com/pneuvial/sanssouci , making our approach operational.     

多类顾客环境下报童模型中库存分配策略研究

考虑一个报童模型中多类顾客的库存分配问题,将顾客按照他们愿意支付价格的高低划分为不同级别。零售商在销售期初决定产品订货量,并在销售期内决定接受或者拒绝不同顾客的需求,以最大化销售期内的期望总利润。将销售期分成大量足够小的时间单位,通过建立一个反向Bellman动态规划方程,以优化每个时间单位内的库存分配策略,并得到了零售商最优的期初订货量。通过与没有库存分配策略下零售商的期望利润进行比较,算例分析得出库存分配策略可以大幅提高零售商的利润。这主要是因为通过库存分配可以使得零售商从高端顾客中获取更多利润,同时能够减小期初的订货量,以节约采购成本和库存持有成本。     

基于免疫遗传算法和列生成的多项目人力资源调度研究

主要研究列生成法求解带有人力资源约束的多项目多模式进度管理问题。首先根据问题建立了相应的数学模型,模型中考虑了多种约束,如项目对人员能力、水平的不同要求,目标为满足约束的条件下成本最小化,其中包含固定和可变两类成本。模型分解后,按照列生成法流程求解。由于问题的复杂性,采用启发式算法求解每个子问题:首先由基于优先原则的启发式方法给出问题的初始解,再由免疫遗传算法寻优。通过数值实验分析了算法性能、模型改进情况,不同优先原则组合对目标成本和各项目间时间分配的影响。     

论领土法的新发展 ——以国际司法判例为视角

国际司法机构在审理国际领土与边界争端的司法实践中,由于经常、反复适用一些法律标准,因此逐渐发展出了一些有关领土法的国际惯例.这些国际惯例得到了许多国家的认可,并被运用于诸多国际实践之中,成为了国际习惯法.正是在国际司法判例的推动下,现代领土法得到了较大的发展:综合考虑理论成为解决国际领土与边界争端的指导原则,有关陆地划界法律依据的领土法规则逐步明确,判断争议领土归属的有效占领制度逐渐发展为一项习惯国际法规则.     

混合多属性群决策中的群体一致性分析方法

本文针对一类属性值为精确实数、区间数和语言值的混合型多属性群决策问题,提出了一种群体一致性分析方法。在该方法中,计算群体一致度时,首先根据专家提供的评价信息在属性层面上计算专家之间的差异度,再由此得到群体一致度,计算过程中不需进行数据类型转换,避免了数据类型转换造成的信息损失;当群体不一致时,在属性层面上给出相应的调整策略,可以使专家有针对性地修改相应属性上的评价信息,使群体尽快达成一致,同时避免了专家评价信息的过度修改。最后通过一个实例分析验证了该方法的可行性和有效性。     

翻译“创造性叛逆”：一种深度忠实

传统翻译忠实标准强调理想化的忠实而轻视翻译的创造性叛逆,认为创造性叛逆是对忠实标准的背叛,忠实和创造性叛逆是对立的。在梳理文献的基础上,论证了创造性叛逆忠实于翻译主体（即作者、译者和读者）,忠实于文化交流和传播,忠实于翻译自身的发展,从而得出结论：创造性叛逆不仅不是对忠实标准的背叛,反而是深度的忠实。     

论依法行政的评估主体选择

文章在阐述了依法行政引入评估体系的必要性的基础上,分析了法治评估的理论主体,评析了目前中国法治评估主体的实践经验。本文认为,法治评估引进第三方是社会发展的需要,是变管理为治理推行先进的政府与社会关系的需要,是党执政能力提升的表现和根本方略。