Coalition for a Healthier Community: Lessons learned and implications for future work

The Coalition for a Healthier Community (CHC) initiative was implemented to improve the health and well-being of women and girls. Underpinning CHC is a gender-based focus that uses a network of community partners working collaboratively to generate relevant behavior change and improved health outcomes. Ten programs are trying to determine whether gender-focused system approaches are cost-effective ways to address health disparities in women and girls. Programs implemented through coalitions made up of academic institutions, public health departments, community-based organizations, and local, regional, and national organizations, are addressing health issues such as domestic violence, cardiovascular disease prevention, physical activity, and healthy eating. Although these programs are ongoing, they have made significant progress. Key factors contributing to their early success include a comprehensive needs assessment, robust coalitions, the diversity of populations targeted, programs based on findings of the needs assessments, evaluations taking into consideration the effect of gender, and strong academic–community partnerships. A noteworthy impact of these programs has been their ability to shape and impact public, social, and health policies at the state and local levels. However, there have been challenges associated with the implementation of such a complex program. Lessons learned are discussed in this paper.     

An approximation of logarithmic functions in the regression setting

We consider a method of moments approach for dealing with censoring at zero for data expressed in levels when researchers would like to take logarithms. A Box–Cox transformation is employed. We explore this approach in the context of linear regression where both dependent and independent variables are censored. We contrast this method to two others, (1) dropping records of data containing censored values and (2) assuming normality for censored observations and the residuals in the model. Across the methods considered, where researchers are interested primarily in the slope parameter, estimation bias is consistently reduced using the method of moments approach.     

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。     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

Estimation of generalized linear latent variable models

Summary.  Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.     

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。