经验不确定分布一致性检验方法研究

经验不确定分布一致性检验是在不确定分布未知的情况下,利用专家经验数据对不确定分布进行推断的一种非参数检验方法,是不确定统计领域参数估计和回归分析的基础和前提。本文主要研究了几种专家经验不确定分布的一致性检验方法。首先,简述了不确定统计和专家经验数据的基本理论;其次,利用专家经验数据系统分析了两组专家的经验分布是否存在显著性差异的Mann-Whitney U检验法、Kolmogorov-Smirnov检验法、游程检验法和极端反应检验法;再次,研究了多组专家经验数据是否存在显著性差异的Kruskal-Wallis检验法和Jonckheere-Terpstra检验法。并借助Matlab 7.0和SPSS 20,利用概率统计中抽样分布理论进行实证分析。     

基于变结构协整理论的保费预测模型

提出了一种基于变结构协整理论的保费预测建模新方法,所建模型反映了保费和GDP之间的长期静态和短期动态波动的均衡关系.通过确定时间序列突变点,并利用突变点信息提高模型的预测精度,避免了传统的保费预测中经常存在的虚假回归问题.采用该方法对中国年度保费进行了预测分析,结果表明了该方法的有效性.     

不确定理论下的DC型养老金的最优投资策略问题

在不确定理论的框架下,研究确定缴费(DC)型养老金的最优投资策略问题.以最小化二次损失函数为目标,分别在固定缴费和不确定缴费的情形下,建立养老金的最优化模型.利用不确定动态规划法,证明了不确定最优性原理,得出了不确定最优性方程,通过求解不确定最优性方程得到最优给付率和最优投资策略.     

基于累积共识贡献的不确定语言共识决策方法

针对不确定加型语言偏好信息下的群决策问题,提出一种基于累积共识贡献的自适应式语言共识决策方法。首先,将不确定加型语言偏好转化为不确定二元语义偏好,定义个体一致度与个体共识偏度,并利用它们构建确定专家初始权重的优化模型;然后,利用不确定二元语义的可能度构造集结模糊评价矩阵以及方案的集结群体偏好,提出专家累积共识贡献测度和群体共识测度,通过对拥有较少合作的专家权重进行惩罚让群体自适应地达成共识,无需强迫专家修改其观点,提出一种群体共识决策方法对方案排序择优。最后,通过一个算例说明方法的可行性和有效性。     

在不确定观测下离散状态时滞系统的最优滤波

研究了在不确定观测下离散状态时滞系统的最优滤波问题,观测值的不确定性则通过一个满足Bernoulli分布且统计特性已知的随机变量来描述. 一般采用状态增广方法将时滞系统转换为无时滞随机系统, 再利用Kalman滤波器的设计方法解决最优状态估计问题, 但是当系统时滞较大时,转换后的系统状态维数很高, 这样增加了计算负担. 为此,基于最小方差估计准则, 利用射影性质和递归射影公式得到了一个新的滤波器设计方法, 而且保证了滤波器的维数与原系统相同.最后, 给出一个仿真例子说明所提方法的有效性.     

基于最大熵原理的测量不确定度商概率建模及计算

测量数学模型建立后,测量不确定度评定需考虑输入量的不确定度及分布的传递性.若测量模型为幂指数积模型,通常先将输入量线性化,再采用合成方差的方法计算被测量的不确定度,方法计算简便,但未揭示不确定度随分布传递这一现象.针对问题,首先运用最大熵原理,计算测量仪器分辨力已知时,输入量的概率密度函数,其结果显示输入量服从矩形分布;其次重点推导了输入量与被测量之间为商概率模型时,被测量的概率密度函数、估计值和测量不确定度,并给出不确定度评定的具体步骤;最后利用恒流源测量高精密小电阻实例的对比分析,说明了该不确定度评定模型的可行性和有效性.     

基于联系数的钢筋抗拉强度测量不确定度评定新方法

钢筋抗拉强度的不确定度包括:钢筋直径的不确定度分量,拉力的不确定度分量,检测结果重复性引入的不确定度分量,数据修约的不确定度分量等等,因此,测量不确定度是与测量数据相联系的,联系数是处理不确定性问题的一种系统数学理论,可以用联系数来表示测量不确定度,为此,提出一种基于联系数的钢筋抗拉强度测量不确定度评定的新方法.     

一类离散不确定线性时滞系统的鲁棒镇定

研究具有时变不确定参数的离散线性时滞系统的鲁棒控制问题,其中不确定性满足匹配条件,利用Ｌｙａｐｕｎｏｖ确定性理论,提出了鲁棒稳定性控制器一种新的设计方法,得到了这类离散不确定线性时滞系统可鲁棒镇定的充分条件。     

基于概率测度的不确定语言多属性决策方法及应用

针对具有不确定语言信息的多属性决策问题,给出了一种基于语言概率测度的决策分析方法。阐述了不确定语言变量的概念,提出了一种用于处理不确定语言变量的语言概率有序加权平均（linguistic probabilistic ordered weighted averaging,LPOWA）算子。采用LPOWA算子将不确定语言转化为二元语义,再通过ETOWA算子得到每个方案的综合评价值,进而可得到所有方案的排序结果。利用LPOWA算子和ETOWA算子,对辽宁省风险投资企业进行评估和优选。理论分析和计算结果表明：该方法简洁可行,便于应用。     

一类分布鲁棒优化问题的线性化方法及其应用

本文考虑一类特殊的极大极小化问题,即分布鲁棒优化问题.这类优化方法是不同于随机规划和鲁棒优化的一类方法,在这类问题中,不确定变量的概率分布往往是不能精确得知的,只知道概率分布所满足的一些条件,比如一次信息、二次信息以及支撑集合信息等.如此分布鲁棒优化问题便是寻求在所有满足条件的分布中找寻满足最坏可能分布的解.一般情况下,这类优化问题的求解都是NP难的.本文考虑一类简单的情形,即考虑不确定变量的概率分布只满足一次信息、支撑集合信息以及仿射一次信息,通过应用半无限规划问题的对偶性,本文指出这类分布鲁棒优化问题等价于线性规划问题,从而原分布鲁棒优化问题可以应用现成的求解线性规划的方法进行求解.为验证方法的有效性,本文将新方法应用于解决不确定条件下含有交易费用的利率管理问题.     

基于不确定指数O-U过程带有浮动利率模型的亚式期权定价

不确定金融是不确定理论在现代金融领域的一种应用,在解决金融问题中发挥着越来越重要的作用。而利率是一个重要的经济指标,经常受到一些不确定因素的影响,在研究期权定价时,有必要考虑浮动利率。本文提出了一种新的不确定指数Ornstein-Uhlenbeck过程模型,假设利率服从不确定均值回复过程,研究了期权定价问题,运用α-轨道方法,分别推导了亚式看涨期权和看跌期权定价公式。最后,设计了计算期权价格的数值算法,并给出数值算例。     

On comonotonic functions of uncertain variables

Uncertain variable is used to model quantities in uncertainty. This paper considers comonotonic functions of an uncertain variable, and gives their uncertainty distributions. Besides, it proves the linearity of expected value operator on comonotonic functions of an uncertain variable.     

Uncertainty distribution and independence of uncertain processes

Uncertain process is a sequence of uncertain variables indexed by time. This paper presents a concept of uncertainty distribution for describing uncertain process. Some sufficient and necessary conditions are also proved for uncertainty distribution and inverse uncertainty distribution of uncertain process. Finally, this paper proposes an independence definition of uncertain processes and shows some mathematical properties of it.     

Exchange rate uncertainty and trade growth—a comparison of linear and non‐linear (forecasting) models

A huge body of empirical and theoretical literature has emerged on the relationship between foreign exchange (FX) uncertainty and international trade. Empirical findings about the impact of FX uncertainty on trade figures are at best weak and often ambiguous with respect to its direction. Almost all empirical contributions assume and estimate a linear relationship. Possible nonlinearity or state dependence of causal links between FX uncertainty and trade has been mostly ignored yet. In addition, widely used regression models have not been evaluated in terms of ex‐ante forecasting. In this paper we analyse the impact of FX uncertainty on sectoral categories of multilateral exports and imports for 15 industrialized economies. We particularly provide a comparison of linear and non‐linear models with respect to ex‐ante forecasting. In terms of average ranks of absolute forecast errors non‐linear models outperform both, a common linear model and some specification building on the assumption that FX uncertainty and trade growth are uncorrelated. Our results support the view that the relationship of interest might be non‐linear and, moreover, lacks of homogeneity across countries, economic sectors and when contrasting imports vs exports. Copyright © 2005 John Wiley & Sons, Ltd.     

Uncertain random programming with applications

Uncertain random variable is a tool to deal with a mixture of uncertainty and randomness. This paper presents an operational law of uncertain random variables, and shows an expected value formula by using probability and uncertainty distributions. This paper also provides a framework of uncertain random programming that is a type of mathematical programming involving uncertain random variables. Finally, some applications of uncertain random programming are discussed.     

Info-gap forecasting and the advantage of sub-optimal models

We consider forecasting in systems whose underlying laws are uncertain, while contextual information suggests that future system properties will differ from the past. We consider linear discrete-time systems, and use a non-probabilistic info-gap model to represent uncertainty in the future transition matrix. The forecaster desires the average forecast of a specific state variable to be within a specified interval around the correct value. Traditionally, forecasting uses a model with optimal fidelity to historical data. However, since structural changes are anticipated, this is a poor strategy. Our first theorem asserts the existence, and indicates the construction, of forecasting models with sub-optimal-fidelity to historical data which are more robust to model error than the historically optimal model. Our second theorem identifies conditions in which the probability of forecast success increases with increasing robustness to model error. The proposed methodology identifies reliable forecasting models for systems whose trajectories evolve with Knightian uncertainty for structural change over time. We consider various examples, including forecasting European Central Bank interest rates following 9/11.     

基于技术预见德尔菲调查的共性技术课题选择方法研究

共性技术是国家技术发展过程中的关键技术,它对提升国家的产业技术能力与国际竞争力具有重要的推动作用.但是,目前世界各国的技术预见实践中,鲜有对共性技术的识别方法提供一套有效的系统方案.基于多属性决策理论,采用德尔菲调查的方法,提供了一种计算技术课题发展优先度的具体方案,并提出了相关建议.     

Incorporating demand uncertainty and forecast error in supply chain planning models

This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.     

Membership functions and operational law of uncertain sets

Uncertain set is a set-valued function on an uncertainty space, and attempts to model “unsharp concepts” that are essentially sets but their boundaries are not sharply described. This paper will propose a concept of membership function and define the independence of uncertain sets. This paper will also present an operational law of uncertain sets via membership functions or inverse membership functions. Finally, the linearity of expected value operator is verified.     

Multicriteria estimation of probabilities on basis of expert non-numeric,non-exact and non-complete knowledge

A new method of alternatives’ probabilities estimation under deficiency of expert numeric information (obtained from different sources) is proposed. The method is based on the Bayesian model of uncertainty randomization. Additional non-numeric, non-exact, and non-complete expert knowledge (NNN-knowledge, NNN-information) is used for final estimation of the alternatives’ probabilities. An illustrative example demonstrates the proposed method application to forecasting of oil shares price with the use of NNN-information obtained from different experts (investment firms).