科技产业上市公司金融支持效率及其影响因素研究

科技产业是我国产能结构调整的重点,而金融支持在其中具有重要作用。采用DEA-Malmquist方法测度我国科技产业金融支持效率,以此为基础构建面板数据回归模型分析其影响因素。实证研究表明,科技产业金融支持效率存在较大波动,技术产出对产业支撑作用明显,而技术投入对产业的驱动则不稳定;在间接融资受阻情况下,通过直接融资促进股本流通对提升金融支持效率具有积极效果。最后提出相应的对策建议。     

智能控制理论概述

综述了智能控制理论的发展情况.简要介绍了常见的几种智能控制理论.     

What Difference Does a Major Make? The Influence of College Major Field on Persistence by African American and White Students

The results from this study indicate similarities and differences in the factors related to the persistence of White and African American students in their freshman and sophomore years in college. Using random samples of data from students enrolled in public institutions of higher education in a Midwestern state, OLS regression analyses indicated that African American sophomores in the high-demand major fields (e.g., Business, Health, and Engineering/Computer Science) were more likely to persist than were those in other major fields, but there were no statistically significant differences in persistence for African American freshmen in other fields. While major fields were not statistically significant for White sophomores, White freshmen in social sciences or undecided about their majors were less likely to persist. The effects of financial aid packages on persistence varied across race.     

统一有偏估计的Pitman优良性

估计量的优良性是回归模型中的重要内容.本文讨论了在Pitman准则下,岭估计与Stein压缩估计的优良性,得到了岭估计优于Stein压缩估计的一个充分条件.     

The matter of assessor variance in early childhood education—Or whose score is it anyway?

Useful assessment outcomes (as manifest through assigned scores) must show reasonable variation across children because it is that variation that presumably defines children's individual differences. Alternatively it is conceivable that some portion of the variability in assessment outcomes does not reflect child differences but rather differences in the performance of the assessors who carry out assessments. Hierarchical linear modeling is applied in this article to identify the amount of score variation attributable to assessors rather than children. Working with multiple cohorts of Head Start and kindergarten children, score variation is analyzed for measures administered outside of classrooms by extramural assessors and for teacher-administered measures within classrooms. The amount of assessor variance (vs. actual child variance) was negligible as associated with extramural assessors but substantial for teacher assessors, indicating that large portions of the variability in teacher-administered assessments have nothing to do with children's unique performances. Recommendations are provided to assist the interpretation of assessment outcomes in future research and practice.     

城市需水预测算法比较

建立高精度水量预测算法模型,有利于水资源充分利用。以北京市2002-2015年需水量为例,对数据进行相关性分析后选出主要影响因素,然后采用主成分回归法、逐步回归法、灰色模型以及BP神经网络共4种方法进行建模,并用北京市2016年和2017年数据进行模型精度验证。结果表明：4种方法都适合用于城市需水量预测,其中主成分分析和逐步回归分析两种方法主要考虑了多元线性回归存在多重共线性,但是逐步回归模型优于主成分回归模型。将4种模型进行对比验证,BP神经网络模型预测精度最高,平均相对误差达到0.79%,用来预测2016-2017年需水量,预测结果分别为38.66亿m3、39.49亿m3,适合作为城市需水量预测方法。     

咸阳市城区基准地价空间分布与区位因子分析

采用GIS技术与多元回归分析教学方法，以基准地价资料作样本，建立各类基准地价的最优回归方程，对城区不同用地类型的基准地价进行模拟测算。该城区基准地价的空间格局及其与区位因子相关性，是由咸阳市城区商业集聚特征，CBD的空间分布及城市空间形态所决定的。利用各在基准地价最优线性回归方程对城区基准地价所作的测算结果能合理反映城区基准地价空间分布的客观规律，即不同类型基准地价的空间分布皆有明显的向心趋势。     

高新技术企业创新绩效影响因素研究

本文以新产品销售收入为企业创新绩效表达指标,以技术效率、企业规模、政府政策、科技活动投入等为关键影响因素,采用分位回归方法建立了一个研究模型;以上海张江高新技术产业开发区2008-2010年高新技术企业统计数据为基础,同时将所有制因素等虚拟变量作为控制变量,全面分析了上述因素对处于不同创新绩效分位区间企业的影响,并与OLS回归结果进行了比较。基于回归分析结果,给出了研究结论,并提出提升我国高新技术企业创新绩效的政策建议。     

析“3+X”高考模式之功效

本文采用定性和定量相结合的分析方法对正在试行的“3+X”高考模式的实际功效进行了较全面的分析。基于分析结果 ,作者认为 ,“3+X”高考有利于高校选拔新生 ,有利于高校扩大办学自主权 ;对中学实施素质教育而言 ,积极影响是主要的 ;某些负面影响可以通过相应措施加以克服     

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons From a Simulation Study and an Application

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the “surface” method, the “frontier” method, the “binding-score” method, and the “fuzzy instrumental variables” method. This article uses a series of simulations to evaluate the relative performance of each of these four methods under a variety of different data-generating models. Focusing on a two-rating RDD (2RRDD), we compare the methods in terms of their bias, precision, and mean squared error when implemented as they most likely would be in practice—using optimal bandwidth selection. We also apply the lessons learned from the simulations to a real-world example that uses data from a study of an English learner reclassification policy. Overall, this article makes valuable contributions to the literature on MRRDDs in that it makes concrete recommendations for choosing among MRRDD estimation methods, for implementing any chosen method using local linear regression, and for providing accurate statistical inferences.