基于复杂系统综合评价的二重主成分分析法

在分析主成分分析在权系数、降维效果等问题的基础上,提出了对复杂系统的综合评价首先要对指标体系分类的各个方面进行主成分分析,在得到各自方面的评价值后再进行主成分分析,最终得到综合评价值的二重主成分分析法.最后从理论和以上市公司经营业绩评价为例检验了这种方法的有效性.     

我国企业综合实力评价方法研究

文章建立了我国企业综合实力的评价指标体系,包括四个层次和三级系统。在分析主观评价方法与客观评价方法的适用性的基础上,提出了多层次模糊综合评价法、主成分法和聚类分析法的数据处理方法。     

基于改进的主成分分析法的县域竞争力综合评价研究

文章针对传统主成分分析法在复杂系统高维指标评价中的局限性,首先建立了改进主成分分析模型,然后基于改进的模型构建了县域竞争力评价指标体系,最后,给出了该评价系统在我国不同县域竞争力综合评价中的应用实例.结果表明:该研究方法在我国县域竞争力分析评价中具有积极的现实意义.     

基于核主成分分析和加权聚类分析的综合评价方法

针对传统主成分分析在处理非线性问题上的不足,文章阐述了应用核主成分分析进行数据处理的改进方法,并介绍了一种基于核主成分的加权聚类分析的综合评价方法.实验表明,该方法可以改进传统的综合评价方法.     

西部地区工业经济的组合评价

组合评价法是在单一评价法的基础上发展起来的一类综合评价方法。常用的单一评价方法有综合指数法、层次分析法、TOPSIS法、主成分分析、因子分析、灰关联度分析、模糊综合评价等,然而单一评价方法有其自身片面性,组合评价法则是综合应用多种方法对同一指标体系进行评价,综合来     

对主成分分析综合评价方法若干问题的探讨

对主成分分析综合评价方法若干问题的探讨白雪梅,赵松山主成分分析法在社会经济统计分析中的应用越来越广泛,随之也产生了一些值得注意和研究的问题。本文对应用主成分分析进行综合评价时遇到的若干问题进行深入讨论。一、主成分分析与因子分析的联系与区别相当数量的应...     

基于CSPC方法的房地产发展健康状况评价分析

传统的主成分分析进行综合评价存在许多不足,因此,提出基于聚类稀疏主成分分析的综合评价方法,使得评价结果更合理和符合实际,并使用该方法分析了上海、武汉、成都三个城市的房地产健康发展状况。     

一种具有奖惩特征的动态主成分综合评价法

由于利用主成分进行综合评价时，一般是基于当期的截面数据而获得的评价结果，没有考虑到上一期情况的影响，因此该方法为一种静态的综合评价方法。针对这一方面的不足，本文设计了根据主成分分析法得出的主成分综合得分，引入一种奖惩因子将其主成分综合得分转换为主成分排序指数，从而形成了以主成分排序指数为评价依据的动态主成分综合评价法。     

城市发展的动态综合评价

文章指出传统的静态多指标综合评价的缺陷,在考虑城市发展静态指标评价和指标值增长变化两方面的基础上,把主成分分析和理想点法相结合,最后通过浙江省各城市的动态综合评价说明了该方法确实是一种有效的动态评价方法。     

结构化数据的多重SPCA评价模型及其应用

结构化数据的综合评价是综合评价的重要内容之一。针对结构化数据的特点以及主成分分析存在的不足,论文基于稀疏思想提出一种改进方法,采用多重稀疏主成分分析对结构化数据进行评价。最后利用房地产开发评价的实例验证了这种方法的有效性和稳定性。     

Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA–CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA–CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA–CMI–OI, for inferring GRNs. After imputation of missing data, the performances of PCA–CMI and PCA–CMI–OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA–CMI–OI algorithm outperform other imputation methods and PCA–CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.     

基于AHP-DEA的传媒业上市公司财务绩效评价研究

在系统分析传媒业上市公司财务目标的基础上,运用层次分析法对大量的传媒业上市公司财务绩效评价指标进行筛选,构建评价指标体系。运用数据包络分析方法构建了上市公司财务绩效综合评价模型,通过对34家上市公司财务数据分析,得出了财务绩效综合排序、规模收益状况,验证了模型的有效性。     

Robust PCA for high-dimensional data based on characteristic transformation

In this paper, we propose a novel robust principal component analysis (PCA) for high-dimensional data in the presence of various heterogeneities, in particular strong tailing and outliers. A transformation motivated by the characteristic function is constructed to improve the robustness of the classical PCA. The suggested method has the distinct advantage of dealing with heavy-tail-distributed data, whose covariances may be non-existent (positively infinite, for instance), in addition to the usual outliers. The proposed approach is also a case of kernel principal component analysis (KPCA) and employs the robust and non-linear properties via a bounded and non-linear kernel function. The merits of the new method are illustrated by some statistical properties, including the upper bound of the excess error and the behaviour of the large eigenvalues under a spiked covariance model. Additionally, using a variety of simulations, we demonstrate the benefits of our approach over the classical PCA. Finally, using data on protein expression in mice of various genotypes in a biological study, we apply the novel robust PCA to categorise the mice and find that our approach is more effective at identifying abnormal mice than the classical PCA.     

Multiple imputation for continuous variables using a Bayesian principal component analysis

ABSTRACT

We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the PCA model. Using a simulation study and real data sets, the method is compared to two classical approaches: multiple imputation based on joint modelling and on fully conditional modelling. Contrary to the others, the proposed method can be easily used on data sets where the number of individuals is less than the number of variables and when the variables are highly correlated. In addition, it provides unbiased point estimates of quantities of interest, such as an expectation, a regression coefficient or a correlation coefficient, with a smaller mean squared error. Furthermore, the widths of the confidence intervals built for the quantities of interest are often smaller whilst ensuring a valid coverage.     

A comparison of different procedures for principal component analysis in the presence of outliers

Principal component analysis (PCA) is a popular technique that is useful for dimensionality reduction but it is affected by the presence of outliers. The outlier sensitivity of classical PCA (CPCA) has caused the development of new approaches. Effects of using estimates obtained by expectation–maximization – EM and multiple imputation – MI instead of outliers were examined on the artificial and a real data set. Furthermore, robust PCA based on minimum covariance determinant (MCD), PCA based on estimates obtained by EM instead of outliers and PCA based on estimates obtained by MI instead of outliers were compared with the results of CPCA. In this study, we tried to show the effects of using estimates obtained by MI and EM instead of outliers, depending on the ratio of outliers in data set. Finally, when the ratio of outliers exceeds 20%, we suggest the use of estimates obtained by MI and EM instead of outliers as an alternative approach.     

基于LMDI的中国碳排放驱动因素研究

基于扩展的Kaya恒等式及对数平均迪氏指数（LMDI）分析法构建了一个包括能源规模、能源结构、二氧化碳排放因子、能源强度、经济规模、产业结构、人口规模、城乡人口结构和生活水平在内的二氧化碳排放驱动因素叠加分解模型。该模型对碳排放的驱动因素作了完整的分解和系统的量化,可为碳减排路径的选择及政策的制定提供理论依据。实证分析结果表明：经济规模与能源强度是二氧化碳排放最强且稳健的驱动因素;能源结构、生活水平也显示了较强的驱动作用;产业结构、人口规模、城乡结构的历史驱动贡献度相对较小,但驱动力巨大,应予以足够重视。     

中国寿险市场发展的实证分析

我国人寿保险业自８０年代复业以来,总体上一直保持着超高速的发展势头,寿险保费占总保费的比重不断上升,市场规模不断扩大。以中保人寿、平安、太平洋保险公司为主力的中国寿险保费收入在１９９５—１９９７年分别为１６０．９亿、３２４．８亿、６５８．８亿元人民币...     

Missing data in principal component analysis of questionnaire data: a comparison of methods

Principal component analysis (PCA) is a widely used statistical technique for determining subscales in questionnaire data. As in any other statistical technique, missing data may both complicate its execution and interpretation. In this study, six methods for dealing with missing data in the context of PCA are reviewed and compared: listwise deletion (LD), pairwise deletion, the missing data passive approach, regularized PCA, the expectation-maximization algorithm, and multiple imputation. Simulations show that except for LD, all methods give about equally good results for realistic percentages of missing data. Therefore, the choice of a procedure can be based on the ease of application or purely the convenience of availability of a technique.     

风险投资项目投资风险综合评价研究

一、风险投资项目投资风险评价的意义和特点　　风险投资是由专业投资机构在自担风险的前提下 ,通过科学评估和严格筛选 ,向有潜在发展前景的高科技公司或项目投入一定的资本 ,并运用科学的管理方式使风险资本得到增值的一种投资活动 ,具有投资方向的前沿性、投资决策的风险性、投资目的的战略性、投资条件的权益性、投资项目的组合性以及投资运作的周期性等特点。风险投资是知识经济和高科技成果市场化、产业化的重要支持系统 ,被称之为“新经济增长的发动机” ,它通过加速科技成果向生产力的转化推动了高科技企业从小到大、从弱到强的发展…     

SPARSE LOGISTIC PRINCIPAL COMPONENTS ANALYSIS FOR BINARY DATA

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study.