Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.     

Multiscale Combined Forecast of Carbon PriceBased on Mixed Structure Data

Accurate forecast of the carbon trading price is of great significance in promoting the scientific and rational development of carbon trading market. Therefore, this paper proposes a multi-scale combined forecasting method for carbon price based on mixed structure data. First, the Google Index is used to extract the unstructured data related to the carbon price.The dimensions of unstructured data are reduced based on principal component analysis. Then, EMD is employedto the structured data,unstructured data and the carbon trading price to obtain different IMFs, which are reconstructed by the Fine-to-Coarse technique to get low, high frequency sequence and trend sequence. Furthermore, the three items are predicted respectively by using ARIMA, PLS and neural networks according to the features of each scale in time series. Finally, the forecasting results are summed to get the carbon price forecast sequence. The proposed method is used to forecast carbon price in EU. The empirical results show that the prediction accuracy of the model is higher than that of the single prediction method and the prediction method that time series aren’t decomposed by EMD, which is of great applicability.     

Social capital in Croatia: measurement and regional distribution

By using partial least squares path modeling, this paper develops a complex index of social capital, selects Croatia as the case for testing and validating the index, and investigates the regional distribution of social capital. The social capital index is defined as a complex hierarchical structured construct of third-order with social trust, participation, and civism as its core dimensions. The main results indicate that civism is the most important cause of social capital followed by active participation and social trust. As far as the regional distribution of social capital is concerned, there are statistically significant differences across the Croatian regions. Although the lowest or below the national average level of social capital and its dimensions can be mostly found in the least developed regions (with the exception of the capital), the highest level of social capital and its particular dimensions is not achieved in the most developed regions.     

我国CPI波动的影响因素分析

文章从宏观环境中选取四个经济指标构建CPI的影响因素指标体系,并通过社会总供给和总需求之间的数量关系,解释指标体系如何推动物价波动.由于宏观经济指标之间存在很强的相关性,在对CPI的多元回归模型中必然存在较强的多重共线性,因此运用岭回归估计模型参数,定量分析各个因素对CPI的影响,并采用偏最小二乘构建CPI预测模型,对CPI的波动进行拟合.     

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.     

第三方物流选择评价体系的研究

为了给供应链选择第三方物流企业提供科学依据,本文在目前第三方物流评价体系研究现状的基础上,讨论了第三方物流企业评价体系的设计原则和要求,并建立了选择第三方物流企业的评价参考体系的相关指标。     

LISREL与PLS路径建模原理分析与比较

1LISR EL建模原理分析狭义上讲,结构方程建模(SEM)多指线性结构关系模型LISREL(linear Structural R ELationship)。LISR EL这个名称有两个含义,一是由JO¨reskog熏提出的线性结构方程式模型,它是协方差结构模型分析的典型代表;二是表示由JO¨reskog和其合作者SO¨rbom共同开发     

基于PLS分析的中国互联网扩散影响因素研究

基于2000—2011年的时间序列数据,利用偏最小二乘法进行回归建模,对影响中国互联网扩散的因素进行了实证分析,研究结果表明:信息基础设施、经济水平、收入水平、上网设备、科技水平、教育水平、城市化水平、上网费用是影响中国互联网扩散的主要因素。基于2011年的31个省市的截面数据,利用变量投影重要性指标进一步分析了互联网发达地区和欠发达地区网络扩散的主要影响因素,结果表明移动电话普及率、人均可支配收入、电脑普及率是影响发达地区互联网扩散的前三位因素,移动电话普及率、人均GDP、城市化水平是影响欠发达地区互联网扩散的前三位因素。     

Knowledge extraction from a large on-line survey: a case study for a higher education corporate marketing

For a higher education public institution, young in relative terms, featuring local competition with another private and both long-established and reputed one, it is of great importance to become a reference university institution to be better known and felt with identification in the society it belongs to and ultimately to reach a good position within the European Higher Education Area. These considerations have made the university governors setting up the objective of achieving an adequate management of the university institutional brand focused on its logo and on image promotion, leading to the establishment of a university shop as it is considered a highly adequate instrument for such promotion. In this context, an on-line survey is launched on three different kinds of members of the institution, resulting in a large data sample. Different kinds of variables are analysed through appropriate exploratory multivariate techniques (symmetrical methods) and regression-related techniques (non-symmetrical methods). An advocacy for such combination is given as a conclusion. The application of statistical techniques of data and text mining provides us with empirical insights about the institution members’ perceptions and helps us to extract some facts valuable to establish policies that would improve the corporate identity and the success of the corporate shop.     

“肯定列表制度”对中国输日农产品的经济效应

通过运用局部均衡模型研究发现,“肯定列表制度”在短期会使中国贸易福利减少,贸易条件恶化,存在负经济效应;在中长期则有利于出口企业提高技术水平,存在贸易促进作用。应用中国农产品最新出口月度统计数据进行实证分析,并选取代表产品——烤鳗,用计量经济学方法和EVIEWS软件建立多元线性回归模型。在剔除相关因素的影响后,“肯定列表制度”的实施使中国对目烤鳗出口数量减少607．6361个单位,短期负面影响显著。