陕西企业核心竞争力的模糊综合评价

对于核心竞争力的评价,由于多目标子系统,多因素影响,专家的评价也只是一个可能性的选择。这种可能性决定了决策过程及结果中存在模糊性,不确定性。因此,对于此类问题,选择模糊综合评价方法可以最大程度地降低模糊性,提高清晰度。为此,本文选用模糊综合评价方法对陕西企业的核心竞争力做出判定,用AHP法制定企业核心竞争力指标的递阶层次结构,用FAHP方法确定指标权重,建立了一个适用于陕西企业的定量测度的多级多目标的模糊综合评价数学模型,为陕西企业核心竞争力评价提供了一个有效的评价手段。     

基于模糊信息的金融风险评价

金融风险评价关系到区域经济的稳健发展.文章针对金融风险评价过程中存在的不确定性和模糊性问题,提出利用三角模糊数作为金融风险评价表度和评价指标权重标度.在给出金融风险评价指标集合的基础上,构建了一套基于三角模糊数评价标度的金融风险TOPSIS评价方法.采用建立的金融风险评价方法,实证分析了五个区域内的金融风险现状,验证了方法的可操作性和合理性.     

一种基于熵权的未确知测度评价方法及应用

未确知集合是基于拓扑空间概念的一种不确定性集合,基于未确知集合的未确知系统理论与方法是一种处理不确定性信息的数学工具,它具有严谨的推理逻辑,在推理时不造成新的信息损失。文章引入信息熵确定各评价指标权重,并采用置信度识别准则,在未确知集合的基础上建立了未确知测度模型。将此模型应用于建筑安全管理综合评价中,提供了一条定量、定性相结合的建筑安全管理综合评价的新途径,使评价更具客观性,最后用一个实际例子说明此模型的应用。     

融合统计思想的大数据算法

海量化的数据规模作为大数据的第一个特征,带来计算方面的首要挑战。大规模样本不一定可以完全替代总体,因此大数据分析的算法设计不仅要考虑精简计算成本,还要考虑如何刻画估计结果的不确定性。本文以分治自助算法和子集双重自助算法为例讨论兼具计算效率提升和不确定性评价的可并行计算的大数据统计算法设计,通过比较分析探讨设计思想与未来研究方向。     

基于复杂性的供应链复合评价方法

对基于复杂性的供应链的协同评价不仅要考虑经济指标,而且要将供应链的不确定性、可靠性,以及其经济效益和发展潜能进行综合评价.这种协同评价既包含随机性和概率约束,又有时序性要求.这就面临将不确定的度量指标——熵与可靠性指标——可靠度,以及经济指标和发展潜能指标进行复合评价的问题,需要建立适应和满足这种复合评价的教学模型.文章对具有时序性要求的确定性DEA模型进行了改进,引入了熵和可靠度指标,综合集成出熵——随机DEA的供应链协同评价模型.     

浅谈企业核心竞争力的认识与培育

随着全球经济的一体化,企业无论大小都将面对国际化的竞争,且竞争会越来越激烈。企业要在竞争中占据主动并持续发展,不仅要具备企业自身的优势,而且还要充分认识企业核心竞争力,本文试就企业核心竞争力的认识与培育进行探讨。     

上市公司竞争力的综合评价--以陕西省上市公司为例

一、上市公司竞争力评价模型的建立 1.评价指标的选择 财务指标是企业经营绩效的重要体现,所以基于财务指标对上市公司进行竞争力评价是比较合理的.对于生存能力财务指标的选取主要考虑资产的规模、质量和盈利能力;对于发展能力财务指标的选取则侧重资产和获利能力的增长趋向;融资能力主要考虑对投资者的吸引能力和投资潜力.上市公司竞争力的评价指标表1.     

基于模糊数学及网络DEA模型的绿色供应链评价方法

供应链有助于整合上下游企业之间的关系与资源,使企业核心竞争力集聚,要针对性的实施供应链管理就必须首先客观的评价供应链网络的运作效率.网络DEA模型不仅能够评价绿色供应链的绩效,而且能够反映出绿色供应链内部存在的问题.文章基于模糊数学的方法对网络DEA模型进行改进,能够规避传统DEA模型中双重角色因素的影响及不同投入指标必须无量纲化的问题,从而提高绿色供应链评价结果的准确性及便捷性.     

三角模糊信息下的企业融资能力评价

企业融资能力关系到企业的可持续发展和规模扩张,文章针对企业融资能力评价过程中存在的不确定性因素,提出一种基于三角模糊数的企业融资能力TOPSIS评价方法.在给出企业融资能力评价指标体系的基础上,针对评价指标权重信息部分可知的情况下,构建了确定指标权重向量的多目标函数.在获得实数型指标权重后,给出一种基于三角模糊数的企业融资能力评价方法.     

我国上市商业银行竞争力评价体系构建

一、指标体系选择原则 (一)全面性.我国上市商业银行评价指标体系是作为一个系统来研究的,因此,指标的选取一定要具有全面性.不仅是指评价指标体系所涉及的评价内容具有全面性,同时还要包含评价指标种类的全面性即指标的多样性.     

一种基于粗糙集理论的组合预测方法

一、前言组合预测方法的基本原理是博采众多预测方法的优势 ,将多个不同预测模型的预测信息组合以期有效地改善预测模型的拟合能力 ,提高预测精度。而组合信息的一般方式是对各个预测模型进行线性加权平均[1～ 3 ] 。因此 ,组合预测方法的研究工作大部分集中在权系数的确定方法上 ,由此产生了许多组合预测方法。常见的方法有 :基于专家意见、相关分析、误差和最小、模糊数学、灰色理论、人工神经网络、遗传算法、小波分析等的组合预测方法。专家意见法的致命弱点是过分依赖专家的主观判断和经验 ,其结果有时难以令人信服。基于相关分析、误…     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

Bayesian analysis of the Box-Cox transformation model based on left-truncated and right-censored data

In this paper, we discuss the inference problem about the Box-Cox transformation model when one faces left-truncated and right-censored data, which often occur in studies, for example, involving the cross-sectional sampling scheme. It is well-known that the Box-Cox transformation model includes many commonly used models as special cases such as the proportional hazards model and the additive hazards model. For inference, a Bayesian estimation approach is proposed and in the method, the piecewise function is used to approximate the baseline hazards function. Also the conditional marginal prior, whose marginal part is free of any constraints, is employed to deal with many computational challenges caused by the constraints on the parameters, and a MCMC sampling procedure is developed. A simulation study is conducted to assess the finite sample performance of the proposed method and indicates that it works well for practical situations. We apply the approach to a set of data arising from a retirement center.     

Nonparametric tests for stratified additive hazards model based on current status data

Stratified regression models are commonly employed when study subjects may come from possibly different strata such as different medical centers, and for the situation, one common question of interest is to test the existence of the stratum effect. To address this, there exists some literature on the testing of the stratum effects under the framework of the proportional hazards model when one observes right-censored data or interval-censored data. In this paper, we consider the situation under the additive hazards model when one faces current status data, for which there does not seem to exist an established test procedure. The asymptotic distributions of the proposed test procedure are provided. Also a simulation study is performed to evaluate the performance of the proposed method and indicates that it works well for practical situations. The approach is applied to a set of real current status data from a tumorigenicity study.     

Evaluating the Effectiveness of State-Switching Time Series Models for U.S. Real Output

Two types of state-switching models for U.S. real output have been proposed: models that switch randomly between states and models that switch states deterministically, as in the threshold autoregressive model of Potter. These models have been justified primarily on how well they fit the sample data, yielding statistically significant estimates of the model coefficients. Here we propose a new approach to the evaluation of an estimated nonlinear time series model that provides a complement to existing methods based on in-sample fit or on out-of-sample forecasting. In this new approach, a battery of distinct nonlinearity tests is applied to the sample data, resulting in a set of p-values for rejecting the null hypothesis of a linear generating mechanism. This set of p-values is taken to be a “stylized fact” characterizing the nonlinear serial dependence in the generating mechanism of the time series. The effectiveness of an estimated nonlinear model for this time series is then evaluated in terms of the congruence between this stylized fact and a set of nonlinearity test results obtained from data simulated using the estimated model. In particular, we derive a portmanteau statistic based on this set of nonlinearity test p-values that allows us to test the proposition that a given model adequately captures the nonlinear serial dependence in the sample data. We apply the method to several estimated state-switching models of U.S. real output.     

A Semiparametric Bayesian Model for Circular-Linear Regression

Circular data are observations that are represented as points on a unit circle. Times of day and directions of wind are two such examples. In this work, we present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is useful especially when the likelihood surface is ill behaved. Markov chain Monte Carlo techniques are used to fit the proposed model and to generate predictions. The method is illustrated using an environmental data set.     

Infant mortality model for lifetime data

In this paper we introduce a parametric model for handling lifetime data where an early lifetime can be related to the infant-mortality failure or to the wear processes but we do not know which risk is responsible for the failure. The maximum likelihood approach and the sampling-based approach are used to get the inferences of interest. Some special cases of the proposed model are studied via Monte Carlo methods for size and power of hypothesis tests. To illustrate the proposed methodology, we introduce an example consisting of a real data set.     

The Loss Rank Criterion for Variable Selection in Linear Regression Analysis

Abstract. Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularization algorithm, a consistent model selection criterion is proposed to select the best one among this preselected set. The approach leads to a fast and efficient procedure for variable selection, especially in high‐dimensional settings. Model selection consistency of the suggested criterion is proven when the number of covariates d is fixed. Simulation studies suggest that the criterion still enjoys model selection consistency when d is much larger than the sample size. The simulations also show that our approach for variable selection works surprisingly well in comparison with existing competitors. The method is also applied to a real data set.     

A multivariate logit model with marginal canonical association

In this work, a generalization of the Goodman Association Model to the case of q, q > 2, categorical variables which is based on the idea of marginal modelling discussed by Gloneck–McCullagh is introduced; the difference between the proposed generalization and two models, previously introduced by Becker and Colombi, is discussed. The Becker generalization is not a marginal model because it does not imply Logit Models for the marginal probabilities, and because it is based on the conditional approach of modelling the association. The Colombi model is only partially a marginal model because it uses simple logit models for the univariate marginal probabilities but is based on the conditional approach of modelling the association. It is also shown that the maximum likelihood estimation of the parameters of the new model is feasible and, to compute the maximum likelihood estimates, an algorithm is proposed, which is a numerically convenient compromise between the constrained optimization approach of Lang and the straightforward use of the Fisher Scoring Algorithm suggested by Glonek–McCullagh.Finally, the proposed model is used to analyze a data set concerning work accidents which occurred to workers at some Italian firms during the years 1994–1996.