专利价值的模糊综合评价模型

基于以往对专利评价指标及评价体系的探索,文章利用模糊数学方法构建立了一种专利价值综合评价模型。该模型首先通过层次分析法确定各评价指标的权重值;然后计算出各评价指标的隶属度,最终根据最大隶属度原则确定出被评专利价值的等级。利用该模糊综合评价模型为专利价值的定量评价提供了一种面向实际、科学合理、操作性强的新方法。     

基于熵权的改进模糊可变模型在资源可持续评价中的应用

文章基于熵权的概念,对模糊可变评价模型进行改进,使该模型能够有效解决评价标准为区间值对结果的影响,减少主观因素对权重的干扰,获得更为合理准确的评价结果.利用该模型以东莞市为例对其水资源可持续利用程度进行评价,并与其它评价方法进行对比.     

基于优势度识别的激励评价方法研究

针对动态综合评价中的激励问题,文章提出一种基于优势度识别的激励评价方法。其特点在于通过识别被评价对象的自身优势度和竞争优势度确定被评价对象各评价指标的激励量以及评价结果的激励量。首先通过计算被评价对象各评价指标的相对优势水平和贡献度确定各评价指标的发展水平;其次通过规划求解确定被评价对象各评价指标的自身优势度;然后利用自身优势度计算被评价对象的评价结果,通过被评价对象评价结果间的比较确定各被评价对象的竞争优势度;最后根据优势度结果确定被评价对象的激励量。     

大样本评价的定量指标无量纲化方法

文章针对大样本评价的特点,提出一种结合指标原始数据的客观特性与专家主观经验的定量指标无量纲化方法,分别就大样本评价中存在的正指标、负指标、中间型指标及复合型指标等定量指标给出了相应情形下的指标归一化计算模型。实例分析表明,该方法对大样本评价的定量指标处理具有指标评分分布合理、评价结果区分度高、可操作性强等优点。     

基于模糊评价的企业核心竞争力研究

通过对影响企业核心竞争力各指标因素进行分析和研究,提出了企业核心竞争力指标体系构建的原则,并构造了企业核心竞争力的6项一级评价指标及22项二级评价指标,建立了比较全面的核心竞争力评价指标体系;考虑到核心竞争力评价指标的模糊性,通过Delphi法对各级指标进行评价,确认了二级指标的隶属度;应用模糊数学评价方法,采取二级模糊评价模型,计算出评价集;根据"最大隶属度原则"可评价出企业核心竞争力的强弱.     

浙江省高校科研项目绩效评价指标体系研究

根据科研项目绩效评价的四大原则,建立了浙江省高校科研项目绩效评价指标体系,并应用层次分析法(AHP)对指标体系进行主观赋权,采用曲线参数标定法确定指标隶属度,进而采用熵权法确定各指标客观权重,最终计算组合权重。该体系为评价高校科研项目提供了方法和途径,但样本需要进一步扩大与完善、评价方法的实际应用有待进一步验证。     

区域旅游业竞争力评价指标体系的构建

文章综合分析区域旅游发展条件、市场竞争态势、旅游产业定位、产业总体布局等相关因素,建立区域旅游业竞争力指标评价体系.选择合理的方法来进行统计和分析,并通过相关的准确数据以比较恰当的定量分析手段来开展分析研究,能够有效、合理地对旅游竞争力之间的内部关系进行深入、科学的探究.以西南地区四川省为例,就区域旅游业竞争力指标评价体系的意义、构建原则、具体指标选取、权重确定原则等问题进行论述.     

基于半结构性决策理论的投资方案模糊优选方法

文章提出了一种对工程项目投资方案进行决策的新方法.将基于相对隶属度的半结构性模糊优选模型应用于投资项目决策中,采用不同的方法处理定量和定性两类评价指标,在相同的计算标准下得到各投资方案指标特征值的相对优属度.根据多级模糊优选模型求出各个方案对不同优属度级别的相对隶属度,最后根据级别特征值向量求出最优项目方案.分析实例表明该方法用于投资方案决策是可行的.     

我国海洋产业发展水平的云模型综合评价

文章针对海洋产业在发展过程中出现的模糊性和随机性问题,引入云模型这种综合评价方法,通过构建海洋产业发展水平评价指标体系,采用熵权法客观地确定评价指标的权重,依据云模型进行模糊综合评价步骤,利用云模型的三个数字特征——期望、熵和超熵建立评价等级,借助正向云发生器确定模糊隶属度矩阵和在不同隶属度情况下的云模型综合评估值,将指标权重集和模糊隶属度矩阵进行模糊转换,确定了我国近10年海洋产业的发展情况,并对得到的结果进行综合性分析.     

关于数据预处理的一种方法——隶属函数的修改

在可持续发展研究中一般的用隶属函数来进行数据的预处理,本文根据综合评价的理论和方法以及模糊数学有关隶属函数的理论,对该隶属函数进行了修改,并举例证明修改后的隶属函数更为合理。     

An improved fuzzy time-series method of forecasting based on L–R fuzzy sets and its application

Classical time-series theory assumes values of the response variable to be ‘crisp’ or ‘precise’, which is quite often violated in reality. However, forecasting of such data can be carried out through fuzzy time-series analysis. This article presents an improved method of forecasting based on L–R fuzzy sets as membership functions. As an illustration, the methodology is employed for forecasting India's total foodgrain production. For the data under consideration, superiority of proposed method over other competing methods is demonstrated in respect of modelling and forecasting on the basis of mean square error and average relative error criteria. Finally, out-of-sample forecasts are also obtained.     

Evaluating lifetime performance for the Pareto model with censored and imprecise information

A lifetime capability index L _tp has been proposed to measure the business lifetime performance, wherein output lifetime measurements are assumed to be precise from the Pareto model with censored information. In the present study, we study a more realistic situation where the lifetime output data are imprecise. The approach developed by Buckley [Fuzzy system, Soft Comput. 9 (2005), pp. 757–760; Fuzzy statistics: Regression and prediction, Soft Comput. 9 (2005), pp. 769–775] incorporated with some extensions (a set of confidence intervals, one on top of the other), is used to construct the triangular-shaped fuzzy number for the fuzzy estimate of the L _tp. With the sampling distribution of the unbiased estimator of the L _tp, two useful fuzzy inference criteria, its critical value and fuzzy p-value are obtained to assess the lifetime performance. The presented methodology can handle the lifetime performance assessment on the condition that sample lifetime data are involved with imprecise information, classifying the lifetime performance with the three-decision rule. With different preset requirements and a certain degree of imprecise data, we also develop a four quadrants decision-making plot where managers can easily simultaneously visualize several important features of lifetime performance for making a decision. An example of business lifetime data is given to illustrate the applicability of the proposed method.     

基于云理论的统计信息质量评估方法研究

针对统计信息质量评估方法的问题,给出一种基于云理论的统计数据质量评估方法。首先,确定云模型的评价等级语言粒度,对其进行软划分,并根据统计数据质量的评价指标体系从八个维度刻划数据质量评估云模型,利用云模型加权算术平均集成技术构造评价综合云;其次,结合云模型相似性的测度方法,根据综合云与评价等级云模型的相似度判断统计数据质量评估综合云的隶属等级。最后,通过实例表明方法的可行性和有效性,新方法可以作为统计数据质量评估和监管的一个参考。     

Indirect membership function assignment based on ordinal regression

In many fuzzy sets applications, fuzzy membership functions are commonly developed based on empirical or expert knowledge. The equation of a membership function is usually determined somewhat arbitrarily. This paper explores a novel membership function design method based on ordinal regression analysis. The estimated thresholds between ordinal measurement categories are applied to calculate the intersection points between fuzzy sets. These intersection points are further applied to determine the equations of the membership functions. Information distortion due to empirical guess can thus be reduced and more latent information in the fuzzy responses can therefore be captured. A case study investigating the relationship between foster mothers’ satisfaction and the foster time and information provided has been conducted in this research. The applicability and effectiveness of the proposed membership function assignment approach have been demonstrated through several case studies.     

A New Approach to Rank Several Multivariate Normal Populations with Application to Life Cycle Assessment

The need to establish the relative superiority of each treatment when compared to all the others, i.e., ordering the underlying populations according to some pre-specified criteria, often occurs in many applied research studies and technical/business problems. When populations are multivariate in nature, the problem may become quite difficult to deal with especially in case of small sample sizes or unreplicated designs. The purpose of this work is to propose a new approach for the problem of ranking several multivariate normal populations. It will be theoretically argued and numerically proved that our method controls the risk of false ranking classification under the hypothesis of population homogeneity while under the nonhomogeneity alternatives we expect that the true rank can be estimated with satisfactory accuracy, especially for the “best” populations. Our simulation study proved also that the method is robust in the case of moderate deviations from multivariate normality. Finally, an application to a real case study in the field of life cycle assessment is proposed to highlight the practical relevance of the proposed methodology.     

Combining ROC curves using MAMSE weighted distributions

The receiver operating characteristic (ROC) curve can be used to evaluate the properties of a diagnostic test from the distribution of a variable on the healthy and diseased populations. The minimum averaged mean squared error (MAMSE) weights were developed to handle data from different sources by adjusting the relative contribution of each data source. The authors use the MAMSE weights to infer the ROC curve of a diagnostic test based on raw data from multiple studies. The proposed estimates are consistent and Monte Carlo simulations show favourable finite sample performance. The method is illustrated in a case study where progesterone level is used to detect ectopic pregnancies and abortions from other natural causes. The Canadian Journal of Statistics 46: 298–315; 2018 © 2018 Statistical Society of Canada     

Determination of a new mixed variable lot-size multiple dependent state sampling plan based on the process capability index

This article proposes a new mixed variable lot-size multiple dependent state sampling plan in which the attribute sampling plan can be used in the first stage and the variables multiple dependent state sampling plan based on the process capability index will be used in the second stage for the inspection of measurable quality characteristics. The proposed mixed plan is developed for both symmetric and asymmetric fraction non conforming. The optimal plan parameters can be determined by considering the satisfaction levels of the producer and the consumer simultaneously at an acceptable quality level and a limiting quality level, respectively. The performance of the proposed plan over the mixed single sampling plan based on C_pk and the mixed variable lot size plan based on C_pk with respect to the average sample number is also investigated. Tables are constructed for easy selection of plan parameters for both symmetric and asymmetric fraction non conforming and real world examples are also given for the illustration and practical implementation of the proposed mixed variable lot-size plan.     

Inferences for the ratio: Fieller’s interval,log ratio,and large sample based confidence intervals

In sample surveys and many other areas of application, the ratio of variables is often of great importance. This often occurs when one variable is available at the population level while another variable of interest is available for sample data only. In this case, using the sample ratio, we can often gather valuable information on the variable of interest for the unsampled observations. In many other studies, the ratio itself is of interest, for example when estimating proportions from a random number of observations. In this note we compare three confidence intervals for the population ratio: A large sample interval, a log based version of the large sample interval, and Fieller’s interval. This is done through data analysis and through a small simulation experiment. The Fieller method has often been proposed as a superior interval for small sample sizes. We show through a data example and simulation experiments that Fieller’s method often gives nonsensical and uninformative intervals when the observations are noisy relative to the mean of the data. The large sample interval does not similarly suffer and thus can be a more reliable method for small and large samples.     

Quantile regression and variable selection for single-index varying-coefficient models

In this article, a new efficient iteration procedure based on quantile regression is developed for single-index varying-coefficient models. The proposed estimation scheme is an extension of the full iteration procedure proposed by Carroll et al., which is different with the method adopted by Wu et al. for single-index models that a double-weighted summation is used therein. This distinguish not only be the reason that undersmoothing should be a necessary condition in our proposed procedure, but also may reduce the computational burden especially for large-sample size. The resulting estimators are shown to be robust with regardless of outliers as well as varying errors. Moreover, to achieve sparsity when there exist irrelevant variables in the index parameters, a variable selection procedure combined with adaptive LASSO penalty is developed to simultaneously select and estimate significant parameters. Theoretical properties of the obtained estimators are established under some regular conditions, and some simulation studies with various distributed errors are conducted to assess the finite sample performance of our proposed method.     

基于改进熵值法的西部地区城市化水平综合评价研究

从广义城市化的内涵出发,构建了融人口、经济、社会文化和城市建设于一体的城市化水平评价指标体系,采用改进的熵值法,对西部12个省区的城市化水平给予了综合评价,并对其2003-2005年的城市化水平进行综合排序,用以找出各省区的优势和差距,便于针对性地制定提高城市化水平的对策。