风险型决策的一种改进方法──马尔可夫分析法

风险型决策指在环境信息不完备，但各种状态出现的概率已知的条件下作出的决策，其准确性通常高于用先验概率作出的决策．这种决策要冒一定的风险。而不确定型决策是指环境信息不完备，且各种状态出现的概率未知，决策者根据自己的主观判断作出的决策。这种决策的风险更大。贵刊1996年第11期施平同志的《浅谈不确定型决策的改进方法》一文对不确定型决策方法进行了一定的改进，即在各种状态发生的概率未知的情况下．通过一些凭经验和直觉得到的模糊信息，利用期望值最大准则进行决策．实际上，该文的思路是将不确定型决策转化为风段型决策来…     

基于优先度的概率区间型决策

对于不确定条件下决策问题的研究,自然状态发生概率的确定是其难点之一。文章阐述了自然状态发生的概率不能完全确定但可以估计其所在区间的决策问题及其性质,给出了决策方案效益期望值极值的算法和一种新的优先度的算法,最后给出了概率区间型决策问题基于优先度的决策方法。     

基于犹豫概率模糊语言集的改进MULTIMOORA决策方法

为解决犹豫环境中由随机性和模糊性引起的不确定性对实际决策造成偏差的多准则群决策问题,文章提出一种基于犹豫概率模糊语言的改进MULTIMOORA决策方法。首先构建各决策者的犹豫概率模糊语言决策矩阵,利用熵值法与离差最大化和距离最小化法得到决策者权重;利用犹豫概率模糊加权平均算子将各决策者的决策矩阵聚合为综合矩阵,进而通过改进的MULTIMOORA方法得到最终排序结果;最后通过物流园区合作商选择的算例分析验证该算法的可行性和有效性,并与TOPSIS法、VIKOR法及HFL-MULTIMOORA进行对比分析。     

一种信息不完全的随机多准则决策方法

不确定型多准则决策已成为多准则决策研究领域的热点。对于方案的准则值为模糊数和语言值的不确定型多准则决策问题,已经引起国内外学者的广泛关注。然而,在实际决策中存在准则值随自然状态的不同而变化且决策者很难预知未来的真实情况的随机多准则决策。     

模糊条件下资金有限独立方案的选择

方案选择时,构成方案现金流量的数据或计算参数可能不确定.这种不确定可能表现为模糊性,即各期现金流量和计算参教为模糊数.文章研究了模糊条件下如何利用模糊净现值和模糊数排序法选择资金有限的独立方案.文中介绍了模糊净现值的计算公式、模糊数的排序方法,给出了利用模糊净现值和模糊数排序法选择资金有限独立方案的一般过程,并给出了数值案例.     

应对突发事件的动态多目标应急决策模型

文章构建了一种改进型动态多目标应急决策模型.运用马尔科夫方法对突发事件在不同发展阶段各状态等级之间的演化概率进行动态预测,建立多目标的应急方案的评价体系,运用三角模糊理论确定不同等级条件下各评价指标权重,然后计算出不同等级下各应急方案的灰色关联贴近度,以不同发展阶段各等级之间的演化概率作为权重,与不同等级下各个应急方案的模糊灰色关联相对贴近度进行权重集合,评选出不同发展阶段的应急决策最优方案.     

模糊条件下方案净现值的统计分析

研究模糊条件下利用模糊统计分析方法选择评价方案,首先介绍了模糊数、模糊统计分析指标的运算及其排序规则,然后根据模糊条件下方案的类型,探讨了模糊统计分析方法如何应用于方案的选择,最后利用数值案例展示了模糊统计分析方法在方案选择中的应用.该研究为模糊条件下的方案选择提供了一种理论工具.     

众里取大规则下由频率确定属性权重的方法

目前获取属性权重方法,大多数需要决策者对属性直接打分或两两比较或优劣排序,这要求属性数量不宜过多、决策者应对各个属性有充分了解.众里取大规则下由频率确定属性权重的方法要求决策者挑选一个最重要属性,进而通过频率确定属性权重.文章首先视属性赋值为随机变量,依概率期望定义权重公式;然后根据离散选择模型相关结论,推导出属性频率与权重的转换表达式,将其代入权重公式得到属性权重;最后构造卡方统计量,判断权重之间是否具有显著差异.该方法适用于有大量决策者和众多属性的群决策问题.     

离散型贝叶斯网络学习在人力资本评估中的应用

文章以最大化网络为前提,进行贝叶斯基于动态样本变化过程的特征分析,并以样本、样本均值以及网络学习知识结构层级差异分析相应状态下人力资本评估密度函数.并进行相应的参数、结构过程以及概率学习,从中获得基于离散型贝叶斯网络学习的各个条件概率的节点转换态概率总和.结果证实:利用离散型贝叶斯网络学习进行的人力资本评估具有一定程度的适用性,同一离散型样本在不同层级的企业人力资本评估存在不同级别的结果,需要进一步结合综合评估,进行企业人力资本影响因素的排除和评估质量提升.     

基于模糊统计的不确定质量特性过程能力指数计算模型

具有不确定质量特性的过程评价存在许多模糊信息,无法直接估算过程的能力指数,使能力指数在这种场合的应用受到限制.为了利用不确定质量特性的主观评价数据估算模糊过程能力指数,文章提出了利用模糊评分数据估计评价结果的模糊统计特征值的方法,并利用模糊统计特征值计算不确定质量特性过程能力指数,最后将不同模糊能力指数进行比较,分析结果表明文章所采用的模糊能力指数对过程中心偏移及质量损失的方面具有较高的灵敏性.     

An estimator of population total in multiple characteristics and its robustness to product moment correlation coefficient

We developed an alternative estimator for the probability proportional to size with replacement sampling scheme when certain characteristics under study have low correlation with the size measured used for sample selection. The performance of the proposed estimator has been studied with other related alternative estimators by comparing biases and the variances of respective alternative estimators. Most of the alternative estimators assume the knowledge of the product moment correlation coefficient. Therefore an empirical study, with the help of wide variety of populations, has been carried out to study their respective efficiency when correlation coefficient is departed from its true value.     

A sequential sampling plan for the inverse gaussian mean

The inverse gaussian distribution is a flexible model which has been extensively applied in the theory of generalized linear models and accelerated life testing where early failure times predominate. More recently it has received attention in areas such as quality control, and as an underlying model that provides an alternative to the analysis of variance. In reliability testing and acceptance sampling data acquisition is often in the face of scarce resources and may be both costly and time-consuming. In such settings it is desirable to reach a statistically sound decision as quickly as possible. Based on sequential probability ratio tests (SPRT), sequential sampling plans provide one method of arriving at a timely, statistically based decision. A sequential sampling plan for the inverse gaussian process mean when the value of the shape parameter of the density is known is presented in this paper.     

On Some Multiple Decision Procedures for Normal Variances

In this article, we propose a multiple decision procedure to test the homogeneity of normal variances. If the null-hypothesis is rejected, our goal is to select a subset containing the population associated with the largest variance. An approximation for the critical value is obtained by deriving an approximate distribution for a linear combination of independent log-gamma distributed random variables. A lower bound for the probability of correct decision is obtained. We also study the determination of the common sample size in order to satisfy a given probability of correct decision when the largest variance is “sufficiently” larger than the rest.     

Inference based on adaptive grid selection of probability transforms

Probability transform-based inference, for example, characteristic function-based inference, is a good alternative to likelihood methods when the probability density function is unavailable or intractable. However, a set of grids needs to be determined to provide an effective estimator based on probability transforms. This paper is concerned with parametric inference based on adaptive selection of grids. By employing a closeness measure to evaluate the asymptotic variance of the transform-based estimator, we propose a statistical inference procedure, accompanied with adaptive grid selection. The selection algorithm aims for a small set of grids, and yet the resulting estimator can be highly efficient. Generally, the asymptotic variance is very close to that of the maximum likelihood estimator.     

Applications of a probability inequality to ranking and selection and other related problems

A probability inequality of conditionally independent and identically distributed (i.i.d.) random variables obtained recently by the author is applied to ranking and selection problems. It is shown that under both the indifference-zone and the subset formulations, the probability of a correct selection (PCS) is a cumulative probability of conditionally i.i.d, random variables. Therefore bounds on both the PCS and the sample size required can be obtained from that probability inequality. Applications of that inequality to other multiple decision problems are also considered. It is illustrated that general results concerning conditionally i.i.d. random variables are applicable to many problems in multiple decision theory.     

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。     

Bayesian optimal phase II designs with dual-criterion decision making

The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and coprimary endpoints, in single-arm and randomized trials. The decision rule of BOP2-DC is optimized to maximize the probability of a go decision when the treatment is effective or minimize the expected sample size when the treatment is futile. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at www.trialdesign.org .     

Lag length selection in panel autoregression

Model selection by BIC is well known to be inconsistent in the presence of incidental parameters. This article shows that, somewhat surprisingly, even without fixed effects in dynamic panels BIC is inconsistent and overestimates the true lag length with considerable probability. The reason for the inconsistency is explained, and the probability of overestimation is found to be 50% asymptotically. Three alternative consistent lag selection methods are considered. Two of these modify BIC, and the third involves sequential testing. Simulations evaluate the performance of these alternative lag selection methods in finite samples.     

Fuzzy parametric sample selection model: Monte Carlo simulation approach

Over a few decades, regression model has received considerable attention and has been shown to be successful when applied together with other models. One of the most successful models is the sample selection model or the selectivity model. However, uncertainties and ambiguities do exist in the models, particularly the relationship between the endogenous and exogenous variables. Therefore, it will disrupt the ability and effectiveness of the model proceeded to give the estimated value that can explain the actual situation of a phenomenon. These are the questions and problems that are yet to be explored and the main aim of this study. A new framework for estimation of the sample selection model using the concept of fuzzy modelling is introduced. In this approach, a flexible fuzzy concept hybrid with the parametric sample selection model is known as fuzzy parametric sample selection model (FPSSM). The elements of vagueness and uncertainty in the models are represented in the model construction, as a way of increasing the available information to produce a more accurate model. This led to the development of the convergence theorem presented in the form of triangular fuzzy numbers to be used in the model. Consistency is an indicator of effectiveness of the developed models and justified using Monte Carlo simulation. Consistency and efficiency of the proposed model are considered in this study. In order to achieve that condition, a Monte Carlo simulation is used. Hence, the error terms of FPSSM are assumed to follow the normal and the chi-square distributions. Simulation results show that FPSSM is consistent and efficient when its distributions are normal. Instead, the FPSSM by chi-square distribution is found to be inconsistent.