客户生命周期模式研究

客户关系具有周期性,可划分为考察期、形成期、稳定期和退化期四个阶段.交易额和客户利润被选作特征变量来描述客户关系水平,理论分析表明:两者均随生命周期阶段的发展而不断提升,考察期最小,形成期次小,稳定期最大.实证研究检验了该结论的正确性.根据客户关系退出时所处的阶段,客户生命周期模式被分成早期流产型、中途夭折型、提前退出型、长久保持型四种基本类型,每种类型均有不同的成因.     

土家族的机智人物故事

机智人物故事是民间故事中的特殊类型,是以一个机智人物为中心所形成的系列故事,有着不同于一般生活故事和民间笑话的审美特征。土家族机智人物故事有着流传空间相对稳定,主人公因地而异的特点,体现着平衡心理的重要社会功能。     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

网上购物风险来源、类型及其影响因素研究

从风险来源的角度深入地研究了网上购物感知风险类型与其相对强度,对于企业从风险来源上降低和管理网上风险具有重要的指导意义.文章发现消费者在网上购物时,风险主要来源于产品本身、远距离交易、互联网这种交易模式和网站这四个方面.研究结果揭示了,当消费者在网上购物的时候,所感受到最强烈的三个风险分别是,来自于产品本身的产品绩效风险、来自于远距离交易的服务风险和来自于网站的来源风险.最后,研究还发现,消费者网上购物感知风险的强弱与消费者的年龄、网络购物频率之间存在较强的相关性,年龄越大,感知风险越强;网上购物频率越高,感知风险越低.     

供应链风险产生根源的理论分析

根据现有文献研究成果,结合实际调研分析,将供应链风险因素整理归纳为系统风险、供应风险、物流风险、信息风险、财务风险、管理风险、需求风险和环境风险。每种风险因素并非独立存在,而是相互影响、互为根源。其非线性叠加结果将会放大供应链整体风险水平。因此,必须认识到其存在的风险,只有在明确各种风险产生根源的基础上,才能恰当地制定、选择和采取有效措施,规避风险,确保供应链整体绩效稳步提高。     

Interpreting DNA Mixtures with Related Contributors in Subdivided Populations

Abstract.  In this paper, we study the statistical interpretation of forensic DNA mixtures with related contributors in subdivided populations. Compact general formulae for match probabilities are obtained for two situations: a relative of one tested person is an unknown contributor of a DNA mixture; and two related unknowns are contributors. The effect of kinship and population structure is illustrated using a real case example.     

鲁迅人格特质的形成及其在作品中的体现

鲁迅先生受家庭变故 ,家庭成员以及自身感情经历和社会环境等的影响 ,形成了其独具特色的人格特质———超越时代的叛逆精神、锲而不舍的意志力和独具慧眼的审美情趣 ,并以伟大的人格特质通过作品的渲染构建社会的理想人格     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.     

融贯中西与古今文化诗学的意象体系——鲁迅诗歌意象的类型分析

意象是诗歌的生命。鲁迅诗歌使用了大量的审美意象,融贯中西与古今文化诗学,形成了自己独立的体系。鲁迅诗歌意象为六种类型,以三组对立的形态出现,即:历史性意象/现实性意象;东方意象/西方意象;社会性意象/人化自然的意象。这些意象的使用,昭示了鲁迅忧国忧民的伟大人格,并形成鲁迅自己独特的诗风。同时,他的诗歌与我国传统诗歌相比较,具有鲜明的现代性,与后起的新诗相比,又具有深厚的传统文化诗学的底蕴。