顾客满意度指数的研究状况及几点思考

本文通过介绍构建顾客满意度指数的动因,以及国内外顾客满意度指数研究的历史、现状和满意度指数体系,对建立中国顾客满意度指数(CCSI)提出了一些看法,展望了我国顾客满意度指数研究的发展方向。     

上证30指数的实证分析

本文通过分析上证30指数、上证综合指数、30指标股股价的统计特征,考察了上证30指数的有效性,并对股价指标数体系的构建提出了意见。     

我国资源节约型社会综合评价指标体系的构建及实证

文章在定义资源节约型社会的基础上,构建了资源节约型社会的综合评价体系;运用模糊综合评价数学模型对我国1990～2006年资源节约型社会的状况进行了综合评价,并计算出了各年相对的综合节约指数。     

经济实力外化与文化软实力增长的综合测评

改革开放以来,我国经济实力显著提升,文化事业也得到较快发展,经济硬实力与文化软实力相互促进、共同增长已成趋势。文章在厘清文化软实力定义的基础上,构建起国家文化软实力综合评价体系及综合评价指数,并就国民生产总值指数对文化软实力综合评价指数的影响程度进行实证分析,得出文化软实力综合评价指数随国民生产总值指数同向变化、国家经济实力的增长可以推动文化软实力增长的结论。     

民生指数评价的理论模型及实证

文章综述了民生指数评价体系的研究现状,从“收入与支出”、“社会保障”、“生活环境”三个方面,构建了一个由三个层次组成的民生指数综合评价体系,并运用探索性因子分析法和聚类分析法对我国31个省(市、自治区)的截面数据进行了实证分析,根据样本数据来确定各项评价指标的权重,并由此建立了民生指数评价模型.     

“对我国综合指数的新思考”的思考——与华婷女士商榷

我国现行的综合指数体系，虽然是一种“方便、实用”的指数体系，但这种指数体系在理论和方法上都“存在着一定的缺陷”，特别是利用这种综合指数体系进行因素分析时，综合物价指数不能真实反映客观变化的实际情况。为此，国内诸多的统计理论工作者对此问题进行了深入的研究，提出了一些改进的方法。华婷女士在贵刊(2003、1、)发表的《对我国综合指数的新思考》一文便是其中一例。     

关于综合指数体系问题的讨论

综合指数体系作为一种分析方法,在统计学原理中占有重要的地位。几年来,人们对综合指数体系尽管作了许多有益的探讨,努力进行了改进,但问题仍然存在:本文试图把综合指数体系问题的来龙去脉作一概述,反映当前的讨论状况。直到七十年代后期,人们一般是用综合指数体系来分析质量指标和数量指标变动对总体变动的影响方向和影响程度。如以p表示质量指标,q表示数量指标,1代表报告期,0代表基期,则综合指数体系的分析公式为:     

统计指数教学技巧探析

统计指数是统计分析的重要方法,也是统计学中的重要内容,它公式多,符号多,内容多,既有绝对数、相对数、平均数的综合运用,又有一套理论性很强的计算方法,但从统计指数全部内容看是有规律可循的,依次包括相互联系的三个方面,即以综合指数编制原理为基础;依据统计资料正确计算统计指数是关键;构建指数体系正确进行因素分析是目的.因此,分析上述三个方面的具体内容,对提高统计指数教学技巧具有重要作用.     

基于压力指数的金融风险预警指标体系构建

文章在分析国外经典预警指标体系的基础上,基于货币危机压力指数构建综合压力指数。通过选取2001年1月至2012年12月的144个月度的数据,对综合压力指数的波动趋势进行分析,对于金融风险预警指标体系构建做了一种新的尝试。     

指数分析理论回顾与总结

本文在传统综合指数体系的基础上，回顾、分析了近年来常见的各类指数分析体系 ，并对它们的特征和运用范畴做了必要的总结和比较。     

Robustness of partial least-squares method for estimating latent variable quality structures

Latent variable structural models and the partial least-squares (PLS) estimation procedure have found increased interest since being used in the context of customer satisfaction measurement. The well-known property that the estimates of the inner structure model are inconsistent implies biased estimates for finite sample sizes. A simplified version of the structural model that is used for the Swedish Customer Satisfaction Index (SCSI) system has been used to generate simulated data and to study the PLS algorithm in the presence of three inadequacies: (i) skew instead of symmetric distributions for manifest variables; (ii) multi-collinearity within blocks of manifest and between latent variables; and (iii) misspecification of the structural model (omission of regressors). The simulation results show that the PLS method is quite robust against these inadequacies. The bias that is caused by the inconsistency of PLS estimates is substantially increased only for extremely skewed distributions and for the erroneous omission of a highly relevant latent regressor variable. The estimated scores of the latent variables are always in very good agreement with the true values and seem to be unaffected by the inadequacies under investigation.     

Explorative Data Analysis and CATANOVA for Ordinal Variables: An Integrated Approach

Categorical analysis of variance (CATANOVA) is a statistical method designed to analyse variability between treatments of interest to the researcher. There are well-established links between CATANOVA and the τ statistic of Goodman and Kruskal which, for the purpose of the graphical identification of this variation, is partitioned using singular value decomposition for Non-Symmetrical Correspondence Analysis (NSCA) (D'Ambra & Lauro, 1989). The aim of this paper is to show a decomposition of the Between Sum of Squares (BSS), measured both in CATANOVA framework and in the statistic τ, into location, dispersion and higher order components. This decomposition has been developed using Emerson's orthogonal polynomials. Starting from this decomposition, a statistical test and a confidence circle have been calculated for each component and for each modality in which the BSS was decomposed, respectively. A Customer Satisfaction study has been considered to explain the methodology.     

Inequality indices as measures of dissimilarity: a generalization

Income Inequlity Indices are shown to be specific instances of Dissimilarity Indices. On the basis of this result various Dissimilarity Indices are defined. This paper was written while the author was visiting the Departement Economie Politique at the University of Geneva, Switzerland.     

中美住房价格指数编制的对比研究

本文以国家统计局编制的“70个大中城市房屋销售价格指数”和美国标准普尔卡斯-席勒住房价格指数为代表,对中美住房价格指数编制的实践进行了对比分析。 在此基础上,对进一步改进与完善我国的住房价格指数提出了自己的看法。     

The Rayleigh–Lindley model: properties and applications

In this paper, the Rayleigh–Lindley (RL) distribution is introduced, obtained by compounding the Rayleigh and Lindley discrete distributions, where the compounding procedure follows an approach similar to the one previously studied by Adamidis and Loukas in some other contexts. The resulting distribution is a two-parameter model, which is competitive with other parsimonious models such as the gamma and Weibull distributions. We study some properties of this new model such as the moments and the mean residual life. The estimation was approached via EM algorithm. The behavior of these estimators was studied in finite samples through a simulation study. Finally, we report two real data illustrations in order to show the performance of the proposed model versus other common two-parameter models in the literature. The main conclusion is that the model proposed can be a valid alternative to other competing models well established in the literature.     

影响新农保女性参保者满意度的因素研究——基于广东平远县的调研证据

借鉴ACSI构建了影响女性参保者新农保满意度因素的分析框架。运用Logistic模型和广东平远县调查数据实证检验发现：新农保待遇水平、参保手续便利性、领取养老金便利性、工作人员服务态度等感知质量和参保者对新农保的了解程度是影响新农保女性参保者满意度的重要因素,因此应通过提高新农保质量水平、加强新农保的政府宣传提高参保者对新农保的满意度。     

Inverse Weibull power series distributions: properties and applications

Inverse Weibull (IW) distribution is one of the widely used probability distributions for nonnegative data modelling, specifically, for describing degradation phenomena of mechanical components. In this paper, by compounding IW and power series distributions we introduce a new lifetime distribution. The compounding procedure follows the same set-up carried out by Adamidis and Loukas [A lifetime distribution with decreasing failure rate. Stat Probab Lett. 1998;39:35–42]. We provide mathematical properties of this new distribution such as moments, estimation by maximum likelihood with censored data, inference for a large sample and the EM algorithm to determine the maximum likelihood estimates of the parameters. Furthermore, we characterize the proposed distributions using a simple relationship between two truncated moments and maximum entropy principle under suitable constraints. Finally, to show the flexibility of this type of distributions, we demonstrate applications of two real data sets.     

The snowball effect of customer slowdown in critical many-server systems

Customer slowdown describes the phenomenon that a customer’s service requirement increases with experienced delay. In healthcare settings, there is substantial empirical evidence for slowdown, particularly when a patient’s delay exceeds a certain threshold. For such threshold slowdown situations, we design and analyze a many-server system that leads to a two-dimensional Markov process. Analysis of this system leads to insights into the potentially detrimental effects of slowdown, especially in heavy-traffic conditions. We quantify the consequences of underprovisioning due to neglecting slowdown, demonstrate the presence of a subtle bistable system behavior, and discuss in detail the snowball effect: A delayed customer has an increased service requirement, causing longer delays for other customers, who in turn due to slowdown might require longer service times.     

企业技术竞争力评价指标体系研究：构建、测评与结论

在分析企业技术竞争力内涵的基础上构建了企业技术竞争力评价指标体系,尝试利用层次分析法对该指标体系进行测评,通过构造判断矩阵并伴随一致性检验,得到了各指标体系的总排序结果。研究表明,从准则层的3项目标来看,企业的技术创新竞争力对总目标的影响权重占有绝对优势;从指标层的29项评价指标来看,影响最大的10项指标在对总目标的影响权重值中占大部分比例,这些指标是提升企业技术竞争力水平的关键所在。     

Evaluating the impact of a grouping variable on Job Satisfaction drivers

An important issue in Job Satisfaction analysis is to discover the most important drivers of the workers’ overall satisfaction. This can be investigated by means of data mining techniques able to measure the importance of a covariate in the prediction of a given outcome. Variable importance measures are mainly proposed in the literature in the framework of tree-based learning ensembles, like Random Forests or Gradient Boosting Machine. In this paper a Random Forest variable importance measure is used for mining the drivers of Job Satisfaction in the Social Service sector. In addition an innovative algorithmic procedure is proposed in order to assess the impact of a grouping variable on this variable importance measure. The goal is to investigate if the importance of a Job Satisfaction driver is different for subjects belonging to different groups.