面向横截面数据的级差最大化灰色指标关联决策模型

针对现有横截面数据灰色关联模型中指标的排序变换影响关联结果的问题,文章从距离的接近性角度,以指数函数的形式表征关联系数;借助TOPSIS思想,并利用级差最大化组合赋权原理分别从正、负理想两个角度对指标进行赋权,集成一种面向横截面数据的级差最大化灰色指标关联决策模型,并对该模型的规范性等性质进行证明。通过实例验证该方法不受指标排序的影响。     

发电厂及变电所悬垂绝缘子最小片数推算

火力发电厂及变电所中悬垂绝缘子串片数的选择是电气设计中必须的步骤和容易产生混乱的难点,因此本文对此进行了研究。同时,对导体和电器选择规程中规定的发电厂及变电所悬垂绝缘子串最小片数再发电厂及变电所污秽等级重新定义后进行修订。     

基于区间灰色区间数的多准则决策方法

将灰色模糊数扩展,定义了区间灰色区间数及其距离.针对准则值为区间灰色区间数的信息不完全的多准则决策问题,提出了一种基于目标规划法的灰色模糊多准则决策方法,求得最优权重向量,从而得到各方案的排序.最后给出一个算例说明方法的可行性和有效性.     

区间型符号数据的因子分析及其应用

符号数据分析是一种新兴的数据挖掘技术,区间数是最常用的一种符号数据。基于误差分析理论,研究针对区间数据的因子分析方法。将区间数看作一个由中点和半径构成的有序偶,并将半径视为区间数的极限误差。对中点样本阵进行因子分析,得到因子得分的中点值。然后将半径样本阵按照误差传递公式,得到因子得分的极限误差。由因子得分的中点值和极限误差最终得到因子得分的区间值。最后以股票的市场综合表现评价问题为案例,进行了应用研究。     

医疗信息共享对患者转移数量和服务质量水平影响研究

当前我国医院间存在严重的"信息孤岛",医院参与医疗信息共享意愿不高,患者的诊疗信息被静态碎片化储存而无法充分有效地利用。考虑到医院进行医疗信息共享将降低患者转移成本,本文构建一个多阶段双寡头动态博弈模型研究医疗信息共享对医院竞争过程中患者转移数量和服务质量水平决策的影响。首先,根据是否存在转移成本,将患者分为新患者和经验性患者,借助Hotelling模型刻画患者的效用函数,分析患者就诊决策。然后,在政府价格规制和不考虑医院利他性情景下,构建了医院累积期望收益目标函数,使用动态规划方法,求解实现医院累积期望收益最大化的服务质量水平,获得了实现患者相互转移且医院在市场中共存的马尔可夫完美均衡。最后,根据医院参与医疗信息共享后患者转移成本降为零,分析与比较信息共享前后患者转移数量和服务质量水平变化。研究发现：在不同医院间本身存在患者转移背景下,医院参与信息共享后,患者转移数量增加但存在一个上限,增加的转移量与患者在医院间的转移成本呈正相关,与初始感知效用的差值范围呈负相关;医院参与信息共享后,均衡状态下的医院服务质量水平高于信息共享前的服务质量水平。因此,在不改变当前医保支付方式下,要加快推进医疗信息共享,政府部门可以根据医院的患者数量和服务质量水平变化对其进行定期补贴,以激励医院积极参与医疗信息共享,本文给出了这个补贴的量化表达。     

Tractable Consideration Set Structures for Assortment Optimization and Network Revenue Management

Discrete‐choice models are widely used to model consumer purchase behavior in assortment optimization and revenue management. In many applications, each customer segment is associated with a consideration set that represents the set of products that customers in this segment consider for purchase. The firm has to make a decision on what assortment to offer at each point in time without the ability to identify the customer's segment. A linear program called the Choice‐based Deterministic Linear Program (CDLP) has been proposed to determine these offer sets. Unfortunately, its size grows exponentially in the number of products and it is NP‐hard to solve when the consideration sets of the segments overlap. The Segment‐based Deterministic Concave Program with some additional consistency equalities (SDCP+) is an approximation of CDLP that provides an upper bound on CDLP's optimal objective value. SDCP+ can be solved in a fraction of the time required to solve CDLP and often achieves the same optimal objective value. This raises the question under what conditions can one guarantee equivalence of CDLP and SDCP+. In this study, we obtain a structural result to this end, namely that if the segment consideration sets overlap with a certain tree structure or if they are fully nested, CDLP can be equivalently replaced with SDCP+. We give a number of examples from the literature where this tree structure arises naturally in modeling customer behavior.     

Assessment of Uncertainty in High Frequency Data: The Observed Asymptotic Variance

The availability of high frequency financial data has generated a series of estimators based on intra‐day data, improving the quality of large areas of financial econometrics. However, estimating the standard error of these estimators is often challenging. The root of the problem is that traditionally, standard errors rely on estimating a theoretically derived asymptotic variance, and often this asymptotic variance involves substantially more complex quantities than the original parameter to be estimated. Standard errors are important: they are used to assess the precision of estimators in the form of confidence intervals, to create “feasible statistics” for testing, to build forecasting models based on, say, daily estimates, and also to optimize the tuning parameters. The contribution of this paper is to provide an alternative and general solution to this problem, which we call Observed Asymptotic Variance. It is a general nonparametric method for assessing asymptotic variance (AVAR). It provides consistent estimators of AVAR for a broad class of integrated parameters Θ = ∫ θ_t dt, where the spot parameter process θ can be a general semimartingale, with continuous and jump components. The observed AVAR is implemented with the help of a two‐scales method. Its construction works well in the presence of microstructure noise, and when the observation times are irregular or asynchronous in the multivariate case. The methodology is valid for a wide variety of estimators, including the standard ones for variance and covariance, and also for more complex estimators, such as, of leverage effects, high frequency betas, and semivariance.     

Modeling Correlated Discrete Uncertainties in Event Trees with Copulas

Modeling the dependence between uncertainties in decision and risk analyses is an important part of the problem structuring process. We focus on situations where correlated uncertainties are discrete, and extend the concept of the copula‐based approach for modeling correlated continuous uncertainties to the representation of correlated discrete uncertainties. This approach reduces the required number of probability assessments significantly compared to approaches requiring direct estimates of conditional probabilities. It also allows the use of multiple dependence measures, including product moment correlation, rank order correlation and tail dependence, and parametric families of copulas such as normal copulas, t‐copulas, and Archimedean copulas. This approach can be extended to model the dependence between discrete and continuous uncertainties in the same event tree.     

宁波高新技术产业发展的影响因素分析——基于灰色关联分析的实证经验

通过对宁波高新技术产业发展影响因素的考察,灰色关联分析的实证研究表明,FDI、人力资源、科技投入是影响宁波高新技术产业发展主要的影响因素,而科技创新载体、知识产权、技术市场和创投资本对产业发展影响不够明显。今后在发挥宁波区位和政策优势的同时,要继续改善产业发展环境,加强薄弱环节建设,促进产业更快发展。