谷歌流感趋势的成功与失误

大数据时代下机遇与挑战并存,如何基于传统方法去处理大数据引人深思,一味地追求大数据也不一定正确。本文以谷歌流感趋势(GFT)为案例,介绍了大数据在疾病疫情监测方面的主要技术及相关成果,阐述了大数据在使用中的关键问题,并结合复杂的统计学工具给出了一些改进措施。谷歌流感趋势的成功取决于相关关系的应用,其失误却来源于模型的构造、因果关系和相关关系的冲突等问题。谷歌流感趋势案例的分析与启示对政府今后在大数据解决方案中有重要的理论和实践意义。     

基于全球化视角的对外技术依存度测算方法及预测

对外技术依存度是我国测度创新型国家建设进程的重要指标。本文提出基于全球化视角的对外技术依存度测算方法,考虑了外资企业的影响,使指标反映的内容更加全面。以大中型工业企业为例,计算了近十年的对外技术依存度,并预测了未来十年的对外技术依存度。分析表明,2020年创新型国家对外技术依存度目标是能够实现的。     

Generating Market Elasticity Estimates Using Cross-Sectional First-Choice and Second-Choice Data

This article develops a method for using first-choice/second-choice information to estimate the ratio of cross-elasticities to own-elasticities. I then use Bayesian techniques to couple this information with prior knowledge of the elasticities. This leads to elasticity estimates reflecting the decision maker's prior knowledge as well as existing data on customer first-choice/second-choice preferences.     

Who Forecasts Better?

This article suggests that the emphasis on forecast evaluations should be redirected from searching for “best” forecasters to finding those who are “better.” A new methodology is developed, and the results indicate that it is possible to identify “better” forecasters.     

Long Memory in Foreign-Exchange Rates

Using the Geweke–Porter-Hudak test, we find evidence of long memory in exchange-rate data. This implies that the empirical evidence of unit roots in exchange rates may not be robust to long-memory alternatives. Fractionally integrated autoregressive moving average (ARFIMA) models are estimated by both the time-domain exact maximum likelihood (ML) method and the frequency-domain approximate ML method. Impulse-response functions and forecasts based on these estimated ARFIMA models are evaluated to gain insight into the long-memory characteristics of exchange rates. Some tentative explanations of the long memory found in the exchange rates are discussed.     

吴汝纶与张之洞课程观之刍议

自从洋务教育开设了"西文"、"西艺"课程之后,就打破了以"四书""五经"一统天下的传统课程格局,但是西学课程尚没有制度化、系统化.经过吴汝纶、张之洞等有识之士的努力,1904年颁行了<癸卯学制>,对西方课程的引进开始制度化.文章通过对学制的奠基人吴汝纶、张之洞二人的课程观进行比较,进而反观中国近代课程的嬗变.     

对经过重建和调整后的我国婴儿死亡率的验证

本文根据婴儿死亡率随人均GDP的动态变化规律筛选最佳验证模型,验证了时间序列模型重新构建的1952－1980年婴儿死亡率和调整校正的1981－1990年婴儿死亡率。结果表明幂函数形式为相对较好的验证模型,拟合精度稍逊于时间序列预测模型,验证模型与时间序列模型的预测结果与历史婴儿死亡率比较,变异程度无显著差异,且预测结果与建立国家儿童死亡监测网络后的国家监测地区婴儿死亡率形成了平稳性过渡。文章认为以时间序列模型重新构建和调整校正的婴儿死亡率比较可靠,更加接近当时的实际水平。     

小区域人口预测模型及其应用——以上海市青浦区为例

参考已有人口预测理论,针对大城市各个区县流动人口较多、人户分离、人口增长较快的特殊情况,构建了小区域人口预测模型,并以上海市青浦区为例,对2011—2020年户籍人口和常住人口的未来发展趋势进行了预测。     

基于蝙蝠算法SVR模型的北京市二手房价预测研究

传统SVR模型可预测房价变化趋势,但不恰当的参数设置会影响预测的精度。本文针对北京二手房同比价格指数的非线性变化特征,将蝙蝠算法(BatAlgorithm,BA)引入到SVR模型中,使其对模型的三个参数进行优化设置,结合网络搜索数据(Web Search Data,WSD),构建了BA-SVR&WSD混合模型,并给出了该模型算法的预测流程,通过引入多个基准预测模型和预测性能度量指标进行对比研究。研究结果表明：基于蝙蝠算法的SVR模型的具有较好的泛化能力、预测效果更准确且预测精度更高,该预测方法也为北京二手房价格的监测和调控提供有价值的参考。     

The Combination Test for Multivariate Normality

A basic graphical approach for checking normality is the Q - Q plot that compares sample quantiles against the population quantiles. In the univariate analysis, the probability plot correlation coefficient test for normality has been studied extensively. We consider testing the multivariate normality by using the correlation coefficient of the Q - Q plot. When multivariate normality holds, the sample squared distance should follow a chi-square distribution for large samples. The plot should resemble a straight line. A correlation coefficient test can be constructed by using the pairs of points in the probability plot. When the correlation coefficient test does not reject the null hypothesis, the sample data may come from a multivariate normal distribution or some other distributions. So, we use the following two steps to test multivariate normality. First, we check the multivariate normality by using the probability plot correction coefficient test. If the test does not reject the null hypothesis, then we test symmetry of the distribution and determine whether multivariate normality holds. This test procedure is called the combination test. The size and power of this test are studied, and it is found that the combination test, in general, is more powerful than other tests for multivariate normality.