Comparing Two-Stage Segmentation Methods for Choice Data with a One-Stage Latent Class Choice Analysis

Market segmentation is a key concept in marketing research. Identification of consumer segments helps in setting up and improving a marketing strategy. Hence, the need is to improve existing methods and to develop new segmentation methods. We introduce two new consumer indicators that can be used as segmentation basis in two-stage methods, the forces and the dfbetas. Both bases express a subject’s effect on the aggregate estimates of the parameters in a conditional logit model. Further, individual-level estimates, obtained by either estimating a conditional logit model for each individual separately with maximum likelihood or by hierarchical Bayes (HB) estimation of a mixed logit choice model, and the respondents’ raw choices are also used as segmentation basis. In the second stage of the methods the bases are classified into segments with cluster analysis or latent class models. All methods are applied to choice data because of the increasing popularity of choice experiments to analyze choice behavior. To verify whether two-stage segmentation methods can compete with a one-stage approach, a latent class choice model is estimated as well. A simulation study reveals the superiority of the two-stage method that clusters the HB estimates and the one-stage latent class choice model. Additionally, very good results are obtained for two-stage latent class cluster analysis of the choices as well as for the two-stage methods clustering the forces, the dfbetas and the choices.     

基于顾客满意度陷阱的市场细分方法研究

提出了基于顾客满意度陷阱的市场细分方法,企业可以根据该细分结果对不同满意程度的顾客采取不同的营销战略,将投向满意顾客的部分资源转向不满意顾客和非常满意顾客,能够更有效地提升顾客忠诚度,并拉动企业的业绩表现。以某个电视制造企业为例,采用了聚类分析、主成分回归分析、多重对应分析等统计分析方法,验证了顾客满意度陷阱的存在,同时表明顾客的人口统计特征与顾客满意作用于顾客忠诚的方式存在对应关系,从而佐证了方法的合理性。     

Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies

In the social science disciplines, the assumption that the data stem from a single homogeneous population is often unrealistic in respect of empirical research. When applying a causal modeling approach, such as partial least squares path modeling, segmentation is a key issue in coping with the problem of heterogeneity in the estimated cause–effect relationships. This article uses the novel finite-mixture partial least squares (FIMIX-PLS) method to uncover unobserved heterogeneity in a complex path modeling example in the field of marketing. An evaluation of the results includes a comparison with the outcomes of several data analysis strategies based on a priori information or k-means cluster analysis. The results of this article underpin the effectiveness and the advantageous capabilities of FIMIX-PLS in general PLS path model set-ups by means of empirical data and formative as well as reflective measurement models. Consequently, this research substantiates the general applicability of FIMIX-PLS to path modeling as a standard means of evaluating PLS results by addressing the problem of unobserved heterogeneity.     

Q型系统聚类分析中的统计检验问题

Q型系统聚类分析已经越来越成为人们广泛应用的多元统计分析方法。然而在应用中盲目套用系统聚类分析方法的情况很多，并对聚类分析方法的适用性、聚类过程的合理性、聚类结果的有效性等问题分析重视不够，更谈不上对聚类分析进行统计检验。因此，为了更好地应用Q型系统聚类分析，就应对Q型系统聚类分析结果进行统计检验并建立统计检验体系。Q型系统聚类分析统计检验体系主要包括：Q型系统聚类分析结果的有效性检验；聚类分析类（组）数选择合理性检验；聚类变量的显著性检验。     

Segmentation,alignment and statistical analysis of biosignals with application to disease classification

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.     

列联资料的有向相关聚类分析

将相关分析和有向聚类分析结合,提出有向相关聚类方法。先依据相关性进行变量合并,再进行有向聚类,分析结果更合理,聚类过程更简单。将该方法用于大学生健康成长影响因素的调查数据,得出更合理的结果。     

生产分割、知识产权保护与出口技术复杂度———基于生产阶段分割的研究视角

国际分工模式已发生根本性改变,全球价值链分工正成为当下世界经济运行的新特征。本文首先基于生产阶段分割法识别与测度了全球主要经济体各自国内和国际生产分割的具体情况,基于此深入考察了生产分割对出口技术复杂度的影响。研究发现,无论是国内生产分割还是国际生产分割,均能够显著提升一国(地区)出口技术复杂度水平。该结果通过一系列内生性和扩展性分析检验,结论依然稳健。另外,在调节效应检验部分,本文还发现知识产权保护对于高技术产品的生产具有不可忽视的调节作用,表明知识产权保护不仅对一国(地区)出口技术复杂度的提升具有直接促进作用,而且还能强化全球生产分割对出口技术复杂度的效果。本文为中国产业向全球价值链中高端攀升提供了理论支持。     

市场分割与地区经济收敛关系的实证分析——基于动态面板纠偏最小二乘虚拟变量法的估计

使用动态面板纠偏虚拟最小二乘虚拟变量法,对市场分割同地区经济收敛间的关系进行实证研究发现:中国经济不存在绝对收敛,在控制了物质资本存量和人力资本后,条件收敛特征明显;在控制了市场分割和经济开放程度后,收敛特征更加显著;市场分割对经济增长的影响呈现非线性特征:当市场分割程度较低时市场分割程度的提高有利于经济增长;当市场分割程度较高时,市场分割程度的提高则会阻碍经济增长.此外,国内市场和国际市场之间存在替代关系.     

Deconvolution methods for non-parametric inference in two-level mixed models

Summary.  We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency.     

基于新三维客户细分模型的线上会员客户价值研究

从客户的当前价值、潜在价值、忠诚度价值三个方面出发,建立适用于线上企业的会员客户价值评价指标体系。同时,在传统RFM模型的基础上构建了新三维的客户细分模型,并根据改进的客户价值细分维度进一步细分客户,利用主成分分析法计算每个客户群的价值得分。最终选取某网站的会员客户数据进行算例分析,通过识别不同客户群体、价值和状态,为企业实施精准营销提供更加细致的客户细分依据。     

生产分割、服务业开放与技术追赶——基于微观视角的理论与实证分析

本文从理论与实证验证了生产分割促进了技术进步，服务业开放扩大了生产分割对技术进步的促进作用。首先，本文构建了理论模型，揭示了生产分割的细化提高了企业专业化生产的程度，使企业将资源集中于核心技术的升级，最终缩小技术差距；服务业开放通过降低信息成本，提高生产分割对技术进步的边际效应。其次，本文使用2000-2013年工业企业数据库，首先计算了企业生产工序数指标，用来准确刻画企业生产分割细化程度；然后构建了技术距离指标，度量了中国14年间制造业技术追赶的动态变化；同时，本文也使用了近14年的服务业外商投资管理目录，阐述了中国服务业外资参与度的动态变化。实证结果发现：首先，本文确定了生产分割有效促进了技术进步，主要体现在缩小了与国际前沿的技术差距，并且服务业外资开放有效促进了这一效应，与理论假设吻合；通过区分信息服务业对服务业开放的支撑渠道，本文做了详细的机制检验，并使用多个指标检验了信息服务业开放的“润滑剂”效应更多体现在低产品替代性企业、知识密集型企业以及高技术相似度的企业中；最后，本文对可能存在的内生性以及估计偏误进行检验，结果证实了研究结果的稳健性。     

Regression analysis of clustered interval-censored failure time data with linear transformation models in the presence of informative cluster size

This paper discusses regression analysis of clustered interval-censored failure time data, which often occur in medical follow-up studies among other areas. For such data, sometimes the failure time may be related to the cluster size, the number of subjects within each cluster or we have informative cluster sizes. For the problem, we present a within-cluster resampling method for the situation where the failure time of interest can be described by a class of linear transformation models. In addition to the establishment of the asymptotic properties of the proposed estimators of regression parameters, an extensive simulation study is conducted for the assessment of the finite sample properties of the proposed method and suggests that it works well in practical situations. An application to the example that motivated this study is also provided.     

主成分聚类分析有效性的思考

 本文针对经典聚类分析和普通主成分聚类分析极端情形下的失效问题展开讨论,通过定义客观赋权的主成分距离为分类统计量,并以实证检验取得良好效果为依据,有效地解决了主成分聚类分析在极端情形下所不能揭示的问题。     

Stratified analysis of correlated binary outcome data: a comparison of model dependent and robust tests of significance

Model dependent and robust test statistics constructed using a generalized estimating equations extension of logistic regression applicable to the analysis of correlated binary outcome data are shown to have relatively simple algebraic expressions in stratified analyses where all variables are measured at the cluster level These expressions are used to demonstrate the close relationship to standard procedures which assume that subjects responses are independent, to prove that the asymptotic validity of model dependent test statistics is assured if the average correlation between cluster members is constant, and that this assumption can be relaxed when there are the same number of subjects in each cluster.     

Neighborhood graphs,stripes and shadow plots for cluster visualization

Centroid-based partitioning cluster analysis is a popular method for segmenting data into more homogeneous subgroups. Visualization can help tremendously to understand the positions of these subgroups relative to each other in higher dimensional spaces and to assess the quality of partitions. In this paper we present several improvements on existing cluster displays using neighborhood graphs with edge weights based on cluster separation and convex hulls of inner and outer cluster regions. A new display called shadow-stars can be used to diagnose pairwise cluster separation with respect to the distribution of the original data. Artificial data and two case studies with real data are used to demonstrate the techniques.     

Constrained clustering of irregularly sampled spatial data

Many spatial data such as those in climatology or environmental monitoring are collected over irregular geographical locations. Furthermore, it is common to have multivariate observations at each location. We propose a method of segmentation of a region of interest based on such data that can be carried out in two steps: (1) clustering or classification of irregularly sample points and (2) segmentation of the region based on the classified points.

We develop a spatially-constrained clustering algorithm for segmentation of the sample points by incorporating a geographical-constraint into the standard clustering methods. Both hierarchical and nonhierarchical methods are considered. The latter is a modification of the seeded region growing method known in image analysis. Both algorithms work on a suitable neighbourhood structure, which can for example be defined by the Delaunay triangulation of the sample points. The number of clusters is estimated by testing the significance of successive change in the within-cluster sum-of-squares relative to a null permutation distribution. The methodology is validated on simulated data and used in construction of a climatology map of Ireland based on meteorological data of daily rainfall records from 1294 stations over the period of 37 years.     

An approach to setting up a national customer satisfaction index: the Jordan case study

The aim of this paper was to develop a national customer satisfaction index (CSI) in Jordan and to derive its theory using generalized maximum entropy. During the course of this research, we conducted two different surveys to complete the framework of this CSI. The first one is a pilot study conducted based on a CSI basket in order to select the main factors that comprise the Jordanian customer satisfaction index (JCSI). Based on two different analyses, namely nonlinear principal component analysis and factor analysis, the explained variances in the first and second dimensions were 50.32 and 16.99% respectively. Also, Cronbach coefficients α in the first and second dimensions were 0.923 and 0.521, respectively. The results of this survey suggests the inclusion of loyalty, complaint, expectation, image and service quality as the main CS factors of our proposed model. The second study is a practical implementation conducted on the Vocational Training Corporation in order to evaluate the proposed JCSI. The results indicated that the suggested components of the proposed model are significant and form a good fitted model. We used the comparative fit index and the normed fit index as goodness-of-fit measures to evaluate the effectiveness of our proposed model. Both measures indicated that the proposed model is a promising one.     

Identifying influential observations in hierarchical cluster analysis

In a cluster analysis of a multivariate data set, it may happen that one or two observations have a disproportionately large effect on the analysis, in the sense that their removal causes a dramatic change to the results. It is important to be able to identify such influential observations, and the present paper addresses this problem. To do so, we must first quantify the effect of a single observation. Various definitions are discussed, and criteria for identifying influential observations are investigated; the minimum spanning tree and the number of neighbours of each observation are considered. The investigation concentrates on single-link cluster analysis, although complete-link analysis is also briefly discussed. Patterns emerge in both real and simulated data, which suggest ways of predicting observations with no effect and those with the greatest effect. It is not necessary to recalculate the results with each observation omitted—an economy of presentation as well as labour.