模糊信息多目标决策中统计方法的应用

在社会经济管理等众多的领域中存在着大量含模糊信息的多目标决策问题,目前对于多目标决策研究的有效方法已有进展。文章针对目标决策提出了模糊选择阵法进行多目标决策,模糊综合评判在统计决策的应用,进而提出来用相对隶属度来统计决策出最优方案等三种有效的统计决策方法,并进行实例分析。     

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.     

基于模糊统计的不确定质量特性控制图研究

不确定质量特性采用专家评分、语言评价等测量手段,使常规的控制图方法难以适用.文章以不确定质量特性评分数据为基础,提出了评判模糊数统计特征的求取方法,并以特征数构建不同类型的评判模糊数隶属函数,以过程均值模糊数割集的上下限作为控制线,以评分样本模糊数割集的上下限为控制图描点数据,以描点数据与控制线之间的位置关系进行异常报警,实现不确定质量特性控制图的绘制,最后在Matlab环境下,以具体的应用实例验证了方案的可行性.     

企业统计能力模糊综合评价方法及应用

文章在明确企业统计能力的内涵的基础上,设计了企业统计能力评价指标体系,提出了运用层次分析法确定权重、运用模糊综合评价方法对企业统计能力进行综合评价的方法步骤,并对烟台市工业企业统计能力进行了综合评价和分析.     

基于正态模糊数回归分析模型的构建与评价

文章采用模糊最小二乘法,求解自变量为精确数、因变量和回归系数均是正态模糊数的一元线性模糊回归模型,证明所求得的模糊估计量具有的统计性质:线性性与无偏性.给出模糊回归模型的残差、残差平方和及拟合优度公式.将方法应用于一个实际问题,并与经典回归分析进行比较,验证了该参数估计方法的合理性与有效性.     

基于模糊动态聚类分析的道路拥挤收费决策模型

文章选取了东、中、西部13个城市影响交通拥挤程度的指标和数据，通过构建模糊等价矩阵进行模糊动态聚类分析，并采用F统计量确定出了最佳分类阈值和最佳分类结果。根据分类结果提出了实施拥挤收费的对策分析，以实现城市交通系统的可达性、机动性和可持续性目标。     

混合评价标度下的经济增长评价方法

经济增长评价过程中存在定性和定量双重评价指标,文章通过分析现有经济增长评价指标体系中的不足之处,给出了新的经济增长评价指标集.针对经济增长中的定性指标,采用区间数和模糊数刻画的评价中存在的不确定性和模糊性;对经济增长的定量指标,采用统计方式获得实数型评价值,针对同时存在实数、区间数和模糊数的混合型评价数据,提出一种基于TOPSISI方法的经济增长评价方法.     

基于语言值的公务员绩效模糊综合评价模型

综合传统考核方法的主要问题,考察公务员绩效评价的基本原则,比较分析传统数字统计和模糊语言值统计的差异,指出经典方法产生的歧义,运用模糊数运算,构建基于语言值模糊数的公务员绩效考核方法。     

基于DEA的模糊点数据回归分析

对每个训练数据的DEA有效值作为它的模糊隶属度,以表示训练数据对总体的贡献程度。从DEA的视角,获取模糊点数据,得到了基于模糊点数据的最小二乘估计,及其一些优良的统计性质。利用平均相对贴近度,给出了一个评价模型拟合效果的准则。通过数值实例的计算比较,表明了该方法的有效性。     

基于语言值的公务员绩效模糊综台评价模型

综合传统考核方法的主要问题,考察公务员绩效评价的基本原则,比较分析传统数字统计和模糊语言值统计的差异,指出经典方法产生的歧叉,运用模糊数运算,构建基于语言值模糊数的公务员绩效考核方法.     

A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix

In this paper, a new test for the equality of the mean vectors between a two groups with the same number of the observations in high-dimensional data. The existing tests for this problem require a strong condition on the population covariance matrix. The proposed test in this paper does not require such conditions for it. This test will be obtained in a general model, that is, the data need not be normally distributed.     

Some asymptotic properties of point estimation with n-dimensional fuzzy data

Some asymptotic properties of point estimation with n-dimensional fuzzy data with respect to a special L ₂-metric ρ are investigated in this article. It is shown that the collection of all n-dimensional fuzzy data endowed with the ρ-metric is a complete and separable space. Some criterions for point estimation in such fuzzy environments are proposed, and the sample mean, variance and covariance with n-dimensional fuzzy data under these criteria are further studied.     

教师课堂教学质量综合评价方法研究

教师课堂教学质量综合评价以课堂教学质量评价指标体系为基础,综合评价方法主要包括指标加权平均法和模糊综合评价法。难点是评价指标的设计和量化处理,权集的构造。对评价结果的分析可采用绝对评价(等级判断),相对评价,动态评价(个体差异的评价)。     

Locally optimal test for simultaneously testing the mean and variance of a normal distribution

A generalization of the locally most powerful unbiased (LMPU) test for the single parameter case to the k-parameter case was proposed by SenGupta and Vermeire (1986). In particular we defined a locally most mean power unbiased (LMMPU) test based on the mean curvature of the power hypersurface. Compared to the type C tests of Neyman and Pearson and the type D tests (Isaacson, 1951), LMMPU tests possess better theoretical properties and enjoy ease of construction of critical regions. In this paper we present an interesting example of a two-parameter univariate normal population for which Isaacson (1951, p. 233) was unsuccessful in finding a type D test. For the case of one observation, we prove that no Type D region exists but the LMMPU test is obtained - it is an example of a test with singular Hessian matrix for its power but is nevertheless a strictly locally unbiased (LU) test.     

Construction of Fuzzy Control Charts Based on Weighted Possibilistic Mean

The problem of constructing control charts for fuzzy data has been considered in literature. The proposed transformation approaches and direct fuzzy approaches have their advantages and disadvantages. The representative values charts based on transformation methods are often recommended in practical application. For representing a fuzzy set by a crisp value, the weight of importance of the members assigned with some membership levels in a fuzzy set should be considered, and the possibility theory can be employed to deal with such problem. In this article, we propose to employ the weighted possibilistic mean (WPM), weighted interval valued possibilistic mean (WIVPM) of fuzzy number as a sort of representative values for the fuzzy attribute data, and establish new fuzzy control charts with WPM and WIVPM. The performance of the charts is compared to the existing fuzzy charts with a fuzzy c-chart example via newly defined average number of inspection for variation of control state.     

Order test for high-dimensional two-sample means

We propose a new method to test the order between two high-dimensional mean curves. The new statistic extends the approach of Follmann (1996) to high-dimensional data by adapting the strategy of Bai and Saranadasa (1996). The proposed procedure is an alternative to the non-negative basis matrix factorization (NBMF) based test of Lee et al. (2008) for the same hypothesis, but it is much easier to implement. We derive the asymptotic mean and variance of the proposed test statistic under the null hypothesis of equal mean curves. Based on theoretical results, we put forward a permutation procedure to approximate the null distribution of the new test statistic. We compare the power of the proposed test with that of the NBMF-based test via simulations. We illustrate the approach by an application to tidal volume traces.     

An operative extension of the likelihood ratio test from fuzzy data

In the present paper we are going to extend the likelihood ratio test to the case in which the available experimental information involves fuzzy imprecision (more precisely, the observable events associated with the random experiment concerning the test may be characterized as fuzzy subsets of the sample space, as intended by Zadeh, 1965). In addition, we will approximate the immediate intractable extension, which is based on Zadeh’s probabilistic definition, by using the minimum inaccuracy principle of estimation from fuzzy data, that has been introduced in previous papers as an operative extension of the maximum likelihood method.     

Nonparametric inference for panel count data with competing risks

In survival and reliability studies, panel count data arise when we investigate a recurrent event process and each study subject is observed only at discrete time points. If recurrent events of several types are possible, we obtain panel count data with competing risks. Such data arise frequently from transversal studies on recurrent events in demography, epidemiology and reliability experiments where the individuals cannot be observed continuously. In the present paper, we propose an isotonic regression estimator for the cause specific mean function of the underlying recurrent event process of a competing risks panel count data. Further, a nonparametric test is proposed to compare the cause specific mean functions of the panel count competing risks data. Asymptotic properties of the proposed estimator and test statistic are studied. A simulation study is conducted to assess the finite sample behaviour of the proposed estimator and test statistic. Finally, the procedures developed are applied to a real data arising from skin cancer chemo prevention trial.     

Robustness of tests for directional mean

In this paper we study the robustness of the likelihood ratio, circular mean and circular trimmed mean test functionals in the context of tests of hypotheses regarding the mean direction of circular normal and wrapped normal distributions. We compute the level and power breakdown properties of the three test functionals and compare them. We find that the circular trimmed mean test functional has the best robustness properties for both the above-mentioned distributions. The level and power properties of the test statistics corresponding to these functionals are also studied. Two examples with real data are given for illustration. We also consider the problem of testing the mean direction of the von-Mises–Fisher distribution on the unit sphere and explore the robustness properties of the spherical mean direction and likelihood ratio test functionals.     

Evolving possibilistic fuzzy modelling

This paper suggests an evolving possibilistic approach for fuzzy modelling of time-varying processes. The approach is based on an extension of the well-known possibilistic fuzzy c-means (FCM) clustering and functional fuzzy rule-based modelling. Evolving possibilistic fuzzy modelling (ePFM) employs memberships and typicalities to recursively cluster data, and uses participatory learning to adapt the model structure as a stream data is input. The idea of possibilistic clustering plays a key role when the data are noisy and with outliers due to the relaxation of the restriction on membership degrees to add up unity in FCM clustering algorithm. To show the usefulness of ePFM, the approach is addressed for system identification using Box & Jenkins gas furnace data as well as time series forecasting considering the chaotic Mackey–Glass series and data produced by a synthetic time-varying process with parameter drift. The results show that ePFM is a potential candidate for nonlinear time-varying systems modelling, with comparable or better performance than alternative approaches, mainly when noise and outliers affect the data available.