因子模型在风险预算分析中的应用

风险预算在国外正赢得学术界、养老基金和资产管理领域等许多方面的广泛关注,并认为是组合管理的一种创新。国内投资者日益认识到投资组合管理与风险控制的重要意义,并近乎成为他们的第一要务。本文利用因子模型,通过风险预算这一投资管理和风险控制技术与方法,讨论投资组合因子风险分解并进行实证分析。     

江苏省基层医务人员工作压力研究

对905名基层医务人员进行问卷调查,了解基层医务人员工作压力情况及其影响因素,为建立适合基层卫生机构的良性人才发展机制提供依据。基层医务人员整体工作压力得分为56.54分;单因素方差分析显示,不同性别、年龄、婚姻状况、学历、职称、机构类型、工作量、工作年数、受患者尊重程度和社会地位的医务人员工作压力得分差异具有统计学意义;多元线性回归分析结果显示,社会地位、工作量、工作年数、受患者尊重程度、最高学历和性别对基层医务人员工作压力的影响具有统计学意义。需要着重关注乡镇卫生院医生的工作压力情况,重点应放在提升基层医务人员社会地位、及时关注中坚力量等方面,从而减轻其工作压力,提高工作积极性。     

西部山区农民易地扶贫搬迁意愿的影响因素

从现在到2020年是打赢脱贫攻坚战的关键时期,从农民自身角度研究搬迁意愿,对加快实施易地扶贫移民搬迁具有重要意义。研究依据西方微观人口迁移理论：地方效用理论、计划行为理论和效价[CD*2]期望理论,整合构建了中国国情下由政府引导的易地扶贫搬迁意愿的理论分析框架。以陕南移民搬迁为例,在验证性因子分析的基础上,运用有序Logit模型和最优尺度模型对理论假设进行检验。结果显示：①心理因素、政策和预期因素、环境因素对搬迁意愿具有显著影响 ②控制变量中家庭人口数、非农劳动力比例、家庭成员求学婚姻等控制变量与搬迁意愿显著相关;③没有搬迁的主要的顾虑是搬迁后找不到工作、城镇生活成本高、搬迁成本高、搬迁后的适应等问题。研究建议政府部门在搬迁补助、就业安置、搬迁后适应等方面加大支持力度,增强搬迁的信心。     

基于低碳发展的生态物流绩效评价研究——以重庆市为例

运用因子分析法对重庆市2006—2015年生态物流绩效进行评价。研究发现：近十年重庆市生态物流保持着越来越好的发展态势;重庆物流业过去单一追求规模和数量的扩张对生态物流绩效造成了负面影响;同时,政府对环保的立法和监管对生态物流的发展也起到了促进作用。最后,根据分析的结果提出对策建议。     

企业农产品电子商务能力测度的实证研究

低水平的农产品电子商务能力阻碍了农产品企业的可持续发展和创新。基于社会物质主义理论,构建企业农产品电子商务能力的模型,讨论农产品电子商务能力的内涵、形成机制和作用效果;并对316份企业样本数据,运用内容分析、问卷调查法和结构方程分析方法对理论模型进行实证检验。结果发现,农产品电子商务能力具有多维度结构,包含管理能力、技术能力和人才能力三个核心维度,且三者相互关联,共同对农产品电子商务能力产生作用。由此开发出量表以期为企业提升农产品电子商务能力提供一个可操作和量化的通用工具。     

基于学习动机感知的学生主体分类及其影响因素探究——以江苏省普通高校为实证

以大样本问卷调查为基础,采用多种数理统计方法,进行基于学习动机主观感知为学生主体类型划分及影响因素的研究。发现学习动机表征因素不止于内在和外在,还会体现在近期和远期、物质和精神、自我和他者等其他复杂要素上,而且还会受到入校时间、学校类型、性别以及地缘背景等因素影响。构建与学生主体需求相匹配的教育资源供给梯次和分类指导对促进高校教育具有重要意义。     

Profession and Workplace Expectations of Social Workers: Implications for Social Worker Subjective Well-Being

The expectations people have of social workers, and those that social workers hold of themselves, can negatively affect social worker's perceived well-being, via work stress and strain and other negative workplace experiences. This qualitative research presents findings that help to better understand what aspects of these expectations have the greatest impact on the well-being of a sample (n = 19) of social workers with reported low levels of workplace and profession satisfaction. Our data support a conceptual framework of social worker's expectations that relate to outcomes of practice, social worker roles, the perceived functioning by other health-related professionals, conflict in direct practice with social worker identity and having to work in adverse situations, as all contributing to the well-being of social workers. Findings are discussed in relation to social work practice and the role and function of professional associations and educational programmes in addressing these issues that emerge in practice.     

水平井分段多簇压裂缝间干扰研究

水平井分段多簇压裂在现场得到了广泛运用，其压裂过程中普遍存在缝间干扰现象。缝间干扰有助于形成复 杂裂缝网络以提高储层导流能力，但是也会导致起裂困难，甚至形成砂堵。因此有必要对分段多簇压裂的缝间干扰问 题进行研究。对此，基于弹性力学建立了分析多簇裂缝诱导应力的数学模型，从起裂压力、裂缝宽度、簇间距等多方面 研究了缝间干扰对水平井分段多簇压裂施工的影响。模拟结果显示，诱导应力会导致起裂压力升高、裂缝变窄，严重 时将造成压裂施工失败。通过进行分析，给出了起裂过程及延伸过程中缝间干扰的影响关系。分析认为，利用缝间干 扰提高改造体积时应当控制簇间距防止对压裂施工造成负面影响。研究结论对优化水平井分段多簇压裂设计具有指 导意义。     

Marginal likelihood estimation from the Metropolis output: tips and tricks for efficient implementation in generalized linear latent variable models

The marginal likelihood can be notoriously difficult to compute, and particularly so in high-dimensional problems. Chib and Jeliazkov employed the local reversibility of the Metropolis–Hastings algorithm to construct an estimator in models where full conditional densities are not available analytically. The estimator is free of distributional assumptions and is directly linked to the simulation algorithm. However, it generally requires a sequence of reduced Markov chain Monte Carlo runs which makes the method computationally demanding especially in cases when the parameter space is large. In this article, we study the implementation of this estimator on latent variable models which embed independence of the responses to the observables given the latent variables (conditional or local independence). This property is employed in the construction of a multi-block Metropolis-within-Gibbs algorithm that allows to compute the estimator in a single run, regardless of the dimensionality of the parameter space. The counterpart one-block algorithm is also considered here, by pointing out the difference between the two approaches. The paper closes with the illustration of the estimator in simulated and real-life data sets.     

Infill asymptotics for adaptive kernel estimators of spatial intensity

We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a spatial point process. We derive asymptotic expansions for the bias and variance under the regime that n independent copies of a simple point process in Euclidean space are superposed. The method is illustrated by means of a simple example and applied to tornado data.