多元化集成模式下交通基础设施建设融资风险识别研究

从单一模式向多元化集成模式转变已成为新形势下拓宽交通基础设施建设融资渠道的必然选择,在规避单一模式局限、形成多元模式优势合力的同时,也加剧了融资不确定性和复杂性,从而对融资风险识别提出了更高的要求。综合考虑多元化集成模式下交通基础设施建设融资风险的多源性、关联性、模糊性、随机性等典型表征,设计了与典型表征相匹配的融资风险识别框架,并构建了融资风险识别两阶段模型。在该模型中,明确了融资风险因素确定流程和维度划分规则,并提出了基于随机二元语义DEMATEL的风险因素关联分析方法进行融资风险因素的研判与诊断。最后,以“PPP+ABS”模式下大成西黄河大桥收益权计划为例开展了计算实验,验证了所构建模型的有效性,并给出了融资风险应对与化解的启示与建议。研究成果能够为相关利益主体明晰融资风险因素构成、研判融资风险因素影响力、诊断融资风险因素可控性、有效应对与化解融资风险提供系统性解决方案。     

Generic Identifiability of Linear Structural Equation Models by Ancestor Decomposition

Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modelling multivariate joint distributions. The models correspond to mixed graphs that include both directed and bidirected edges representing the linear relationships and correlations between noise terms, respectively. A question of interest for these models is that of parameter identifiability, whether or not it is possible to recover edge coefficients from the joint covariance matrix of the random variables. For the problem of determining generic parameter identifiability, we present an algorithm building upon the half‐trek criterion. Underlying our new algorithm is the idea that ancestral subsets of vertices in the graph can be used to extend the applicability of a decomposition technique.     

基于非线性-主成分Logistic回归的会计舞弊识别研究

混沌理论认为,人类行为大多具有非线性特征。会计舞弊属于行为会计的研究范畴,而传统上基于统计理论构建的舞弊识别模型大多受限于线性约束假设,可能存在模型设定偏误和信息提取不充分的缺陷。以沪深A股受到监管处罚的上市公司及其配对公司为样本,借鉴Taylor展开式的非线性思想,并使用主成分分析消除变量多重共线性,构建了非线性-主成分Logistic回归的会计舞弊识别模型。与线性回归模型对比发现,前者具有更高的舞弊识别正确率,模型拟合度更优。应用这一模型有助于更加充分提取舞弊识别信息,提高舞弊识别效率。     

Time substitution and network effects with an application to nanobiotechnology policy for US universities

We present a dynamic network model of the knowledge production process for nanobiotechnology research at 25 US universities during 1990–2005. Universities produce knowledge outputs in nanobiotechnology consisting of Ph.D. graduates, research publications, and patents. Inputs include the university’s spending on R&D in engineering and the life sciences, and the university’s own stock of knowledge measured by past publications in nanobiotechnology. In addition, universities take advantage of the stock of knowledge produced by other universities in previous periods. We simulate the effect of the National Science Foundation being able to optimally allocate research funds for nanobiotechnology research between universities and across time so as to maximize the aggregate amounts of the three knowledge outputs produced by the universities.     

基于SVR的股市预测与择时研究

本文首先阐述支持向量回归机原理,在此基础上建立了SVR预测模型,以HS300指数数据为测试样本,分析了SVR模型在时间序列预测问题中的优势,并在此基础上进行了交易实验.结果表明：支持向量回归机适用于预测股市大盘的短期走势,并能够得到比较好的预测效果.     

公共空间新型盲道设计研讨

在公共空间规划中,盲道的合理布局与设计能充分体现对眼疾患者的关心与呵护。通过对比国内外盲道发展规律,指出了未来盲道发展趋势。同时,佐以无线射频技术（RFID）等作为技术支撑,进一步探讨如何在新型盲道设计中便于盲人获取信息与后期维护,并构建了设计模型与具体方案。     

中美大学生核心价值观教育的比较与思考

大学生的核心价值观教育在任何国家都是国民教育的重中之重,目的在于培养公民的价值理性和价值共识,促进社会和谐。中美两国在大学生核心价值观教育的本质属性、内容、方法、途径等方面有异有同,比较研究有助于提升我国社会主义核心价值观教育的成效。目前国内仍有部分核心价值观教育比较研究偏向于肯定甚至主张照搬美国的价值观教育模式,忽视了近年来中国在大学生核心价值观教育各个方面与时俱进的变化与特色;比较研究过程中首先应当保持绝对客观和适度自信,毕竟美国只是开展教育比较研究所需要的典型之一,而不是绝对的典范。     

淮南市生态足迹演变分析

为了了解淮南市生态随经济社会发展的演变特征,为淮南市制定生态可持续发展对策提供依据,依据生态足迹模型、运用时间序列分析法对淮南市2007-2014年间的生态足迹进行研究.研究表明:生态赤字逐年增加,淮南市生态处于加剧不可持续发展状态.建议采取以下管控措施:通过转变能源利用方式、优化产业结构、适当控制人口规模、提倡低碳生活等途径减少生态足迹,通过保护自然资源、提高各类资源的生产效率等途径提高生态承载力,加强生态教育,加强机制建设等.     

Bootstrap Procedures for Online Monitoring of Changes in Autoregressive Models

We compare the behavior of several bootstrap procedures for monitoring changes in the error distribution of autoregressive time series. The proposed procedures are designed to control the overall significance level and include classical tests based on the empirical distribution function as well as Fourier-type methods that utilize the empirical characteristic function, both functions being computed on the basis of properly estimated residuals. The Monte Carlo study incorporates different estimators and a variety of sampling situations with and without outliers.     

Curve of Correlation for Time Series

The curve of correlation is a measure of local correlation between two random variables X and Y at the point X = x of the support of this variable. This article studies this local measure using the theory of time series for bivariate and univariate stationary stochastic process. We suggest local polynomial estimators for time series observing their consistency both theoretically and through simulations. For this, different sizes of series, bandwidths, and kernels, besides lags and models’ configurations were used. Applications have also been made using the daily returns of two financial series.