收益为模糊的区间指派博弈的Owen值

Owen值是合作博弈中比较重要的一种解,而指派博弈恰好是Owen值在现实中的应用。鉴此,提出一类具有模糊收益值的区间指派博弈,并利用区间数的相关理论,在通过建立公理化体系对区间指派博弈的Owen值进行研究的同时,给出一个算例分析并验证其正确性。     

基于左右端点的IOWGA算子的区间型组合预测模型

文章针对实际值序列和预测值序列均为区间数的组合预测问题,将区间数的左右端点作为考虑问题的出发点,引入诱导有序加权几何平均(IOWGA)算子,以对数误差平方和为准则,分别建立左、右端点的IOWGA算子的变权系数最优组合预测模型,并通过引入偏好系数把多目标最优化模型转化为单目标最优化模型,给出各模型间最优解的性质.     

两均匀分布区间长度之比的区间估计

文章给出了均匀分布的最小和最大次序统计量的密度函数。另外,通过构造统计量,给出了两均匀分布区间长度之比的区间估计及其假设检验方法,讨论了通常区间估计和最短区间估计。最后,根据实例求出了这两种区间估计和它们的长度,并得出了相应的结论。     

一种新的基于TOPSIS的区间数排序法

针对有限个区间数的排序问题,提出了一种基于TOPSIS的区间数排序法。首先定义了有限个区间数的正理想区间数和负理想区间数;然后引入传统的TOPSIS思想,定义了区间数相对贴近度的概念,根据区间数的相对贴近度大小可以对任意有限个区间数进行排序。最后通过一个属性权重和属性值均为区间数的多属性决策问题说明了该方法的有效性和可行性。与传统方法比较,此方法具有原理简单,运算方便,实用性强等优点。     

不确定性多属性决策中区间数排序的一种新方法

文章针对区间型多属性决策问题的研究,提出了一种新的区间数排序方法.首先引入极大区间数的概念.然后借助于区间数距离公式给出了区间数比较的一个相对优势度公式.基于优势度矩阵提出了区间数排序的一种简洁算法.最后通过一个算例说明了此算法的有效性.     

ICOWA算子的区间组合预测模型

文章针对区间数的预测问题,将Theil不等系数和诱导连续有序加权平均(ICOWA)算子结合,建立了基于Theil不等系数的ICOWA算子的区间组合预测模型.针对该模型建立了评价体系,并提出了优性区间组合预测、非劣性区间组合预测和劣性区间组合预测等概念,最后通过实例分析,得出该区间组合预测是合理的和有效的.     

基于IGOWLA算子的区间组合预测模型

文章针对实际值序列和预测值序列均为区间数的组合预测问题,将区间数的左右端点作为考虑问题的出发点,引入诱导广义有序加权对数平均算子(IGOWLA)算子的概念,以误差平方和为准则,分别建立左右端点的基于IGOWLA算子的变权系数最优组合预测模型,并通过引入偏好系数把多目标最优化模型转化为单目标最优化模型.通过实例分析说明了该模型能有效提高预测精度.     

对带有区间时变时滞系统的鲁棒稳定性分析

文章针对带有区间时变时滞和不确定性的线性系统的鲁棒稳定性问题,文章通过构造新的Lyapunov泛函、引用自由权矩阵及引入不等式等方法,巧妙处理了区间时滞项和不确定项,以线性矩阵不等式形式给出了具有较小保守性的稳定性的充分条件.仿真例子说明文中所得结果的有效性.     

基于区间关联度的IOWGA算子的区间组合预测

文章利用诱导有序加权几何平均(IOWGA)算子,将区间作为整体,以灰色区间关联度作为诱导准则,可以建立左右端点权重不相等的常规的区间组合预测模型,以及适用于左右端点较接近的左右端点权重相等的区间组合预测模型,避免出现左端点值大于右端点值的极端情形.通过实例分析说明左右端点相等权重和不等权重模型能有效提高预测精度以及模型的灵活性.     

基于合作博弈K-S解的供应链批发价格协调机制的实现

文章首先构建了一个由1个制造商和1个零售商组成的两级供应链和1个简单的批发价格契约,通过对比该供应链在非合作博弈与合作博弈条件下不同的利润获得情况后,得出只要销售的商品为正常品,无论该商品是富有弹性还是缺乏弹性,制造商与零售商进行合作都将会增加供应链系统利润的结论.然后讨论了作为一个合理的分配机制应具备的条件,通过引入合作博弈条件下讨价还价问题的K-S解作为一个合理分配机制,对双方合作后获得的新增利润进行了分配,分配结果使得双方利润比不合作时均有所提高.     

基于联系数贴近度的区间型组合预测模型及其有效性

利用区间数和二元联系数的相互转化关系,把区间数组合预测问题转换成二元联系数组合预测问题。在联系数贴近度的最优准则下,建立基于联系数贴近度的区间型组合预测模型,分析了该模型的有效性理论,包括:基于联系数贴近度的区间型组合预测模型是非劣性组合预测、优性组合预测的充分条件定理,基于联系数贴近度的区间型组合预测模型的冗余预测方法的存在性和冗余方法的判定定理。对某省社会保障水平适度区间值进行组合预测的实证分析,结果显示所建立的模型能有效提高预测的精度。     

A Study of Interval Censoring in Parametric Regression Models

Parametric models for interval censored data can now easily be fitted with minimal programming in certain standard statistical software packages. Regression equations can be introduced, both for the location and for the dispersion parameters. Finite mixture models can also be fitted, with a point mass on right (or left) censored observations, to allow for individuals who cannot have the event (or already have it). This mixing probability can also be allowed to follow a regression equation.Here, models based on nine different distributions are compared for three examples of heavily censored data as well as a set of simulated data. We find that, for parametric models, interval censoring can often be ignored and that the density, at centres of intervals, can be used instead in the likelihood function, although the approximation is not always reliable. In the context of heavily interval censored data, the conclusions from parametric models are remarkably robust with changing distributional assumptions and generally more informative than the corresponding non-parametric models.     

Exact functionals and their core

Exact cooperative games or non-additive measures, coherent lower previsions and coherent risk measures are mathematically essentially the same. They are belong to the class of exact functionals on an arbitrary set of bounded functions. We investigate the exact functionals from a functional analytic point of view, i. e. we characterize this class by a norm, present a Hahn-Banach type theorem, provide a powerful construction method and adopt the concept of the core resp. σ-core from cooperative game theory. Received: December 2000; revised version: May 2001     

Interval estimation for the generalized inverted exponential distribution under progressive first failure censoring

In this paper, we consider the inferential procedures for the generalized inverted exponential distribution under progressive first failure censoring. The exact confidence interval for the scale parameter is derived. The generalized confidence intervals (GCIs) for the shape parameter and some commonly used reliability metrics such as the quantile and the reliability function are explored. Then the proposed procedure is extended to the prediction interval for the future measurement. The GCIs for the reliability of the stress-strength model are discussed under both equal scale and unequal scale scenarios. Extensive simulations are used to demonstrate the performance of the proposed GCIs and prediction interval. Finally, an example is used to illustrate the proposed methods.     

Interval estimation for a binomial proportion: a bootstrap approach

This paper discusses the classic but still current problem of interval estimation of a binomial proportion. Bootstrap methods are presented for constructing such confidence intervals in a routine, automatic way. Three confidence intervals for a binomial proportion are compared and studied by means of a simulation study, namely: the Wald confidence interval, the Agresti–Coull interval and the bootstrap-t interval. A new confidence interval, the Agresti–Coull interval with bootstrap critical values, is also introduced and its good behaviour related to the average coverage probability is established by means of simulations.     

Validation of tolerance interval

The tolerance interval receives very much attention in literature and is widely applied in industry. However, it is generally constructed through the criterion of minimum width by Eisenhart et al. (1947). Although effort for clarification of several prediction related intervals has been made recently by Huang et al. (2010), the appropriateness of the tolerance interval for its role in industry applications is insufficiently discussed. According to manufacturers' requests, a concept of admissibility of tolerance intervals is defined in this paper and we show that these types of tolerance intervals are not admissible due to short of confidence. We further prove that a 100(1−α)% confidence interval of a γ-coverage interval is admissible and is appropriate for use.     

基于前景理论的区间数多准则决策方法

针对准则偏好值为区间数的不确定性决策问题,提出了一种基于前景理论的多准则决策方法。该方法将准则值看作给定区间内N个等差随机数,利用正态分布的分布函数来表示区间内准则值的分布规律,在给定各准则参考点的基础上,通过价值函数和决策权重函数计算方案在各准则下的前景值,并通过加权平均得到各方案的总前景值;根据前景值的大小对所有方案排序并得到最优方案。该方案的可行性通过一个简单实例得到了论证。     

A Classroom Approach to the Construction of an Approximate Confidence Interval of a Poisson Mean Using One Observation

Even elementary statistical problems may give rise to a deeper and broader discussion of issues in probability and statistics. The construction of an approximate confidence interval for a Poisson mean turns out to be such a case. The simple standard two-sided Wald confidence interval by normal approximation is discussed and compared with the score interval. The discussion is partly in the form of an imaginary dialog between a teacher and a student, where the latter is supposed to have studied mathematical statistics for at least one semester.     

Sample Size Determination for a Two-Sided Multiple Components Tolerance Interval

In the conventional concept, the variance of a tolerance interval from the measurements is a single component, and the sample size for quality control process was estimated by the variance of a single component. However, we can find examples in recent about several components that could vary in their measurements, so an approximate method must be found to modify the conventional tolerance interval. In our paper, we develop an approach to calculate the sample size for a two-sided tolerance interval including several components in the variance from the measurements. An example is presented to illustrate our proposed method.