农村社区公共服务设施配置探讨——以江苏省邳州市港上镇前湖村为例

农村公共服务设施主要指教育设施、文化体育设施和社区服务设施。在分析前湖村公共服务设施需求情况的基础上,文章参照农村村庄的各类公共服务设施配置标准和城市社区公共服务设施配置标准中的组团建设标准,提出了前湖村公共服务设施项目的配置标准。     

日本存款保险制度对我国银行稳健经营的借鉴意义

随着金融危机频繁爆发,建立我国显性存款保险制度已势在必行。分析我国目前实施的隐性存款保险制度的种种弊端,借鉴日本存款保险制度的经验,能为我国显性存款保险制度的建立提出相关建议。     

基于支持向量机的旅游客源预测模型研究

为了能够进行合理的旅游规划和管理,应该对旅游客源进行准确的预测,因此,深入地研究了支持向量机在旅游客源预测中的应用。首先,分析了支持向量机的基本理论;其次,分析了近年来我国境内旅游市场发展情况,并且分析了旅游市场变化的原因;然后,进行了旅游客源预测实际分析,首先根据1996-2011年期间我国旅游客数据验证了支持向量机的预测准确性,然后对2012-2018年我国境内旅游客源数量进行预测,从而为旅游机构进行旅游配套设施建立提供了有利的理论依据。     

论我国破产重整程序存在的问题——以英国法为视角

我国2006年颁布的《企业破产法》首次引入了重整制度,试图给陷入财务困境又有重生希望的企业提供一个法律保障机制。但实践中重整程序应用的不多且条文本身也存在着各种问题。在制度设计上,如程序的启动、自动冻结的效果、破产管理人权利的分配、法院的介入以及强制裁定权的运用等方面存在着缺陷。英国公司重整制度自上世纪八十年代以来进行了多次重大修改,其制度的改革和完善给我国立法者提供了很多启示。     

2001年全球国际旅游发展回顾及2002年前景预测

本文根据世界旅游组织公布的统计资料，回顾分析了美国“9．11”事件发生后对2001年全球国际旅游发展带来的严重冲击和影响，以及引起国际区域旅游格局发生较大变化的状况。同时，根据目前国际经济形势变化、区域旅游发展态势和各国采取的对应措施，预测了2002年国际旅游发展的前景。     

Criminal incident prediction using a point-pattern-based density model

Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or “hot spots” observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space–time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA.     

浅论上市公司盈余管理

本文运用统计方法 ,选取有效建模变量 ,建立了Logit预测模型对我国上市公司财务困境进行了预测。研究结果表明该模型具有良好的预测精度 ,可以作为证券投资者和分析人员使用的一种有效的财务困境预测工具。     

Trait Recognition: An Alternative Approach to Early Warning Systems in Commercial Banking

The present study reports the empirical results of trait recognition (TR) as an alternative early warning system for identifying failing US commercial banks. TR has previously been employed in the sciences, and unlike previous statistical and nonparametric models, incorporates a large number of interaction variables based on the independent variables taken two and three at a time. Discriminatory original and interaction variables (or traits) are selectively retained for use in classifying observations based on a voting procedure. Comparative results for failed and nonfailed US commercial banks using Call Report data indicate that the TR model generally outperformed logit regression models, in some cases by a considerable margin. A major implication of these results is that TR could be useful in other binary choice problems in business finance and accounting, including predictions of nonbank failures, bond rating changes, and other firm events.     

CURRENT STATISTICAL ISSUES IN AGRICULTURAL RESEARCH

Four topics, selected from the range of statistical subjects with current relevance for agricultural research are considered. The first is concerned with the need and usefulness of efficient incomplete block designs for any number of treatments, either equireplicate or with different number of replicates. The second one is a presentation of linear prediction procedures for phenomena with spatially random behaviour and its generalization towards kriging. Next, a reparametrization of a general four–parameter family of growth curves is recommended, leading to numerically stable parameter estimation for RICHARDS' functions and additional special cases. Finally, the modelling of factorials with continuous response and of log–linear relationships for frequencies is proposed to be handled on the same footing, with preference for an alternative criteria of model deviance over test critera or C_p–like measures.     

商业银行信用风险评估的生存分析模型及实证研究

企业发生财务危机,不能归还到期贷款是商业银行信贷资产的主要风险来源,商业银行如何构建恰当的信用风险评估模型来预测企业的财务危机,从而避免这类信用风险的出现就显得尤为重要。本文以我国上市公司为研究对象,结合杜邦分析法建立了基于生存分析的信用风险评估模型,模型对于随机选取的预测样本,其提前1年、2年和3年的预测准确率分别达到86%、72%和68%。通过与Altman模型、Ohlson模型预测结果的比较和鲁棒性检验的结果发现,该模型同时具有可以使用时间序列、无需样本配对、中远期预测能力强和高鲁棒性的特点,这些特点特别对于商业银行中长期信贷风险管理具有较高的应用价值。