社会资本对居民生活垃圾分类行为的影响机理分析

社会资本与环境保护合作行为的逻辑关系已经成为环境管理的一个重要研究方向。基于219户城镇居民调查数据的实证研究发现,除了年龄、受教育年限和中共党员身份外,以社会网络、社会规范和社会信任为要素的社会资本,对提高居民生活垃圾分类水平有显著的正向影响。具体而言,社会网络能够降低居民机会主义和“搭便车”的行为倾向;社会规范能够提高居民行为的可预测性,增强居民投资环境保护集体行动的信心;社会信任则通过降低交易成本,促进居民生活垃圾分类的自主合作行为。为从源头上化解我国生态环境管理面临的“垃圾围城”困境,政府应积极促进居民间的网络互动,完善互惠共享的社会规范,提升居民间的普遍信任,同时加大对生活垃圾分类知识和技巧的宣传力度、发挥党员在遵守社会规范中的带头作用,通过社会资本发展促进公众的自主环境保护合作行为。     

论数+形+量结构中量词的特点

现代汉语中数量词中插入形容词的结构即数+形+量结构比较常见,例如,一大张纸,一大群人,一长根铁丝。陆俭明先 生对这种语言现象进行了详细的描写,但是还是留下了一些问题给大家讨论。例如,为什么数量词中间可以插入形容词,插入 形容词对整个结构有什么影响。从量词的特点和形容词的语义指向来看,数+形+量结构中的量词具有空间性、物质性、描写 性的特点,使得形容词能够进入该结构,形容词的语义指向不同,使整个结构有不同的表达功能。     

一基于SEM的经济发展与社会发展关系——以济南市为例

利用济南市2001—2013年的数据,构建济南市经济与社会发展关系的结构方程模型(SEM),考量经济发展的4个潜变量(经济水平、经济结构、经济增速、经济物耗)与社会发展的4个潜变量(居民的生活质量、社会稳定、人口素质、生态环境)间的相互影响,结果表明,居民生活质量正相关于人口素质和经济水平,人口素质正相关于经济水平;居民生活质量作用于社会稳定,而社会稳定是生活质量的固有要求;生态环境负相关于经济增速和经济物耗。     

基于正交试验的长条薄壁板件注塑工艺参数优化与定量分析

针对塑件成型过程中体积收缩率对成型塑件尺寸精度的影响,文章选取长条薄壁板件作为研究对象,利用AMI有限元分析软件对长条薄壁板注塑成型过程进行数值模拟,采用多因素交互正交试验的方法获得常用ABS塑料在不同的工艺参数下成型薄壁件的体积收缩率。以体积收缩率为研究目标,对试验结果进行定量分析,对比分析每一个工艺参数对研究目标的贡献率,并计算得到最优的工艺参数组合,依据实验结果优化塑件成型工艺。该方法的应用为注塑成型工艺参数优化提供了定量的数据参考,是一种快速而实用的方法。     

多相感应电机任意非正弦气隙磁密的磁动势分析与应用

多相感应电机转矩密度的提高依赖于非正弦气隙磁密行波的合理构建,由于传统的电流相位、幅值控制策略无 法给出期望的非正弦磁密行波,因此文章研究了控制多相感应电机定子相电流中基波和谐波幅值、相位、频率的大小来 构建期望的气隙磁密波形的方法、首先基于单根载流导体产生的气隙磁动势波形,推导了任意相数、不对称、包含任意 高次谐波电流波形激励的具有典型绕组结构的多相感应电机的磁动势分布表达式。根据以上磁动势分析结论,多相电 机可通过变电流相序变极。针对气隙磁密行波逼近方波波形的案例,根据所提出的磁动势分析方法分析和预测了理想 空载励磁电流的波形。将预测波形和方波供电下的励磁电流波形进行了比较,从FFT分析结果来看,仍需微调各次谐波 电压相位,从而实现各次电流相位完全一致。     

Intraday Serial Correlation,Volatility, and Jump: Evidence from China's Stock Market

In this article, we investigate the relationships among intraday serial correlation, jump-robust volatility, positive and negative jumps based on Shanghai composite index high frequency data. We implement variance ratio test to quantify intraday serial correlation. We also measure the continuous part of realized volatility using jump-robust MedRV estimator and disentangle positive and negative jumps using Realized Downside Risk Measure and Realized Upside Potential Measure proposed by Bi et al., (2013 Bi, T., Zhang, B., Wu, H. (2013). Measuring downside risk using high frequency data–realized downside risk measure. Communications in Statistics–Simulation and Computation 42(4):741–754.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). We find that intraday serial correlation are positively correlated with jump-robust volatility and negatively correlated with negative jumps which confirm the LeBaron effect.     

Visualization for Large‐scale Gaussian Updates

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large‐scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large‐scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.     

Temporal Disaggregation: Methods,Information Loss,and Diagnostics

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.     

空间面板杜宾模型的Bootstrap Wald-COMFAC检验研究

将共同因子约束(COMFAC)的Wald检验问题引入到空间面板模型中,讨论空间面板杜宾模型与空间面板误差模型的识别问题。蒙特卡洛模拟表明:在有限样本下,基于渐近临界值的Wald检验有着良好的检验功效,但存在着较为严重的尺度扭曲。进一步采用残差Bootstrap方法,在不损失检验功效的前提下,能够显著地降低检验的尺度扭曲。因此,残差Bootstrap方法是更为有效的检验方法。     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.