生态功能区域农村土地流转行为影响因素研究

厘清农户土地流转行为的主要影响因素,推进流转意愿与流转行为相匹配,对于生态功能区域提升土地利用效率、保障生态用地供给具有重要意义。基于河北省农户调研数据,将二元Logistic回归模型和ROC曲线相结合,深入分析农户土地流转行为的影响因素,并依据ROC曲线检验模型效果和界定最佳预测阈值。模型结果显示ROC曲线的Youden指数为32.7%,对应的预测阈值为0.41,模型效果较为良好,研究结果表明家庭人口规模、承包土地规模、主要收入来源、土地归属认知、村庄地形、村庄类别、租金是影响农户土地流转行为的显著性影响因素,农户土地流转行为发生的影响因素并不限于经济因素的考量。     

陕西农户健康投资意愿影响因素分析

文章基于对陕西农村的462个农户的实地调查,分析了影响农户健康投资意愿的因素。结果表明,农户收入水平、劳动力身心健康程度、劳动力的文化教育水平、劳动力外出务工、农户家庭结构、新型农村合作医疗制度、家庭信贷可获性、医疗机构水平及可及性因素对农户健康投资意愿有着不同程度的影响。因此,当地政府在农村推行医疗保障制度过程中,应充分考虑农民的健康投资意愿,制定更为合理的政策。     

林业政策对林权抵押贷款信用风险影响的实证分析

以198份银行信贷员的调查资料为依据,通过建立多类别逻辑回归模型,分析林业政策对林权抵押贷款信用风险的影响。研究结果表明林权登记管理政策、限额采伐管理政策、林权交易管理政策对林权抵押贷款信用风险的影响显著,指出政府应通过健全我国林权登记管理政策与完善限额采伐管理政策和林权交易管理政策等,以进一步维护金融交易安全,推动林权抵押贷款持续开展。     

会计信息需求者对企业会计信息质量的影响——来自中国上市公司的经验证据

从会计信息需求的角度,研究企业会计信息的主要需求者对会计信息质量的影响.以2004～2006年在沪深交易所上市的A股上市公司为样本,以会计信息质量为因变量,以股权投资者、债权投资者和企业管理当局为主要考察对象,并选取相关的控制变量进行实证分析,发现我国上市公司的国有股股东、法人股股东、中小股东和债权投资者都未成为可以与会计信息供给方相制衡的有效的会计信息需求主体,而企业管理当局能否成为会计信息的有效需求者,还取决于企业的内部治理机制是否完善.     

城市公共交通出行者交通信息选择行为研究

先进的交通信息服务系统建设是我国城市交通可持续发展的必由之路。文章基于大连市公共交通出行信息需求专项调查,通过多维度频率分布研究,对不同个人属性、信息服务特征等条件下公共交通出行者的交通信息内容需求进行分析,进一步建立公共交通出行者交通信息选择binary logistic模型,对公共交通出行者交通信息选择行为进行研究。研究显示,公共交通出行者交通信息内容需求因个人属性、信息服务特征等因素存在较大差别,交通信息内容对公共交通出行者交通信息选择行为存在不同程度的影响。最后,文章基于实证研究结果,提出面向城市公共交通出行者的交通信息服务系统规范化建设的相关策略。     

成长型科技创业企业的信用评价研究

创业企业经常受到外部的融资约束,对成长型科技创业企业尤其严重.因此,构建成长型科技创业企业的信用评价指标体系和模型非常必要.文章基于企业的动态财务数据,并将企业的成长与创新能力作为重要的评价指标,应用Logistic回归方法,提出了成长型科技创业企业的信用评价模型和方法,来动态评价企业的信用状况.通过模型比较和检验,证明其评价准确度较高.     

“一带一路”背景下中国区域物流网络模型研究

选取“一带一路”重点涉及的五大区域18个地区为研究对象,利用改进引力模型测量区域物流引力大小及地区引力总量,构建区域物流网络模型。在明确现有模型的特点和“一带一路”对物流网络目标要求的基础上,考虑区域物流发展水平和区域间差异情况,给出优化方案及优化后的物流网络模型,指出中国区域物流网络存在的问题并给出合理化建议,旨为政府决策提供参考。     

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.     

Confidence interval estimation of the difference between paired AUCs based on combined biomarkers

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.     

Using relative utility curves to evaluate risk prediction

Summary.  Because many medical decisions are based on risk prediction models that are constructed from medical history and results of tests, the evaluation of these prediction models is important. This paper makes five contributions to this evaluation: the relative utility curve which gauges the potential for better prediction in terms of utilities, without the need for a reference level for one utility, while providing a sensitivity analysis for misspecification of utilities, the relevant region, which is the set of values of prediction performance that are consistent with the recommended treatment status in the absence of prediction, the test threshold, which is the minimum number of tests that would be traded for a true positive prediction in order for the expected utility to be non-negative, the evaluation of two-stage predictions that reduce test costs and connections between various measures of performance of prediction. An application involving the risk of cardiovascular disease is discussed.