非期望产出下我国能源消耗产出效率差异研究

提高能源消耗产出效率是新常态下经济发展的必然趋势,随机扰动和外部环境是研究能源消耗产出效率不可回避的问题,而传统的DEA模型无法剔除非期望产出对效率值的影响。本文通过构建四阶段Bootstrap-DEA-Malmquist模型,采用1990~2014年的省际面板数据作为样本数据,通过松弛指标调整能源投入,剔除外部环境和随机因素对能源效率和能源全要素生产率的扰动,并借助Bootstrap随机抽样法进一步减小由于样本差异造成的影响,从静态和动态两个维度测算了中国能源消耗产出效率和全要素生产率。研究发现,外部环境和随机扰动对能源效率的估算具有显著影响,剔除外部环境影响因素后得到的能源效率值、技术效率变化以及全要素生产率变动均有不同程度的下降,而调整初始投入后的技术进步值有所上升。此外,考虑环境因素时,能源效率值下降,但是进一步运用Bootstrap随机抽样法消除随机因素的影响后,能源效率值有所上升。本文的主要贡献在于将环境因素和随机因素从能源效率与能源全要素生产率的测算中剥离,获得更为准确的能源效率值和能源全要素生产率值。     

Coalition-building and the forging of solidarity across difference and inequality

Diverse coalitions hold great potential for social movements, but they also face tremendous challenges. In this article, I review the literature on diverse alliances with a focus on how trust, commitment, and ultimately, solidarity can be developed and sustained across divides. The article begins by discussing the needs of diverse alliances to build trust and commitment, and the coalitional characteristics deemed vital for doing so, with a focus on shared neutral space, ongoing interaction, and social ties and bridgebuilders. Five coalitional processes and practices are identified and discussed that have been empirically found or theorized to be imperative for cultivating solidarity across difference and inequality. These processes include (a) uniting around shared principles while engaging difference; (b) acknowledging and managing inequalities; (c) making space for each other; (d) attention to managing conflicts; and (e) actions that confirm the shared commitments and negotiated identity. I conclude by evaluating the state of research on developing and sustaining alliances across divides.     

Robustness to non-normality and autocorrelation of individuals control charts

This paper studies the effects of non-normality and autocorrelation on the performances of various individuals control charts for monitoring the process mean and/or variance. The traditional Shewhart X chart and moving range (MR) chart are investigated as well as several types of exponentially weighted moving average (EWMA) charts and combinations of control charts involving these EWMA charts. It is shown that the combination of the X and MR charts will not detect small and moderate parameter shifts as fast as combinations involving the EWMA charts, and that the performana of the X and MR charts is very sensitive to the normality assumption. It is also shown that certain combinations of EWMA charts can be designed to be robust to non-normality and very effective at detecting small and moderate shifts in the process mean and/or variance. Although autocorrelation can have a significant effect on the in-control performances of these combinations of EWMA charts, their relative out-of-control performances under independence are generally maintained for low to moderate levels of autocorrelation.     

文化差异视角下的英汉颜色词的联想

不同的民族由于地理、民俗、宗教及价值观念等方面的差异,在表达同一概念时,词汇被赋予了许多的联想意义。掌握汉英颜色词不同的文化联想意蕴,并且了解产生这些不同联想意义的原因不仅有利于提高英语学习水平,还能促进和加强中西文化之间的交流。     

用工业CT技术测量储层岩芯饱和度分布

认识储层岩石中含油饱和度及饱和度分布,对驱替机理的深入研究是非常重要的。为了搞清储层岩石在驱替过程中的饱和度分布情况,应用工业CT技术,提出了一种储层含油岩芯被驱替后的饱和度分布的CT密度差测量方法。在岩样的同一断面,分别对干岩样、饱和岩样及在驱替后的岩样进行CT扫描,重建相应的断层图象,测量所建图象的灰度值,计算出相应的密度值,用密度差法计算出饱和度及其分布值。在γ射线工业CT机“CD-300BG”上对某油田岩样进行了实际测量,获得的结果说明,采用CT密度差法测量饱和度分布是有效的。该方法与常规驱替法相比较,具有快速、精度高、无损岩样和能模拟地层状态测量等特点,是油田开发、油藏描述和地层评价的一种新的有效方法。     

An Additive–Multiplicative Restricted Mean Residual Life Model

The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.     

Earthquakes occurrences estimation through a parametric semi-Markov approach

The estimation of earthquakes’ occurrences prediction in seismic areas is a challenging problem in seismology and earthquake engineering. Indeed, the prevention and the quantification of possible damage provoked by destructive earthquakes are directly linked to this kind of prevision. In our paper, we adopt a parametric semi-Markov approach. This model assumes that a sequence of earthquakes is seen as a Markov process and besides it permits to take into consideration the more realistic assumption of events’ dependence in space and time. The elapsed time between two consecutive events is modeled as a general Weibull distribution. We determine then the transition probabilities and the so-called crossing states probabilities. We conclude then with a Monte Carlo simulation and the model is validated through a large database containing real data.     

Optimal financing and dividend policy with Markovian switching regimes

This work investigates an optimal financing and dividend problem for an insurer whose surplus process is modulated by an observable continuous-time and finite-state Markov chain. We assume that the insurer should never go bankrupt by issuing new equity. The goal of the insurer is to maximize the expected present value of the dividends payout minus the discounted cost of equity issuance. We obtain the optimal policies and explicit expressions for the value functions when the risk reserve process is modeled by both upward jump model and its diffusion approximation. Numerical illustrations of the sensitivities of the model parameters are provided.     

Hierarchical likelihood approach to non-Gaussian factor analysis

Factor models, structural equation models (SEMs) and random-effect models share the common feature that they assume latent or unobserved random variables. Factor models and SEMs allow well developed procedures for a rich class of covariance models with many parameters, while random-effect models allow well developed procedures for non-normal models including heavy-tailed distributions for responses and random effects. In this paper, we show how these two developments can be combined to result in an extremely rich class of models, which can be beneficial to both areas. A new fitting procedures for binary factor models and a robust estimation approach for continuous factor models are proposed.     

From the Digital Divide to the Connectivity Dividend Difference: A Connectivity Capital Perspective*

The access divide was once the basic form of the digital divide. The development of Internet infrastructure has narrowed the access divide and increased application coverage, but it has also touched off a connectivity dividend difference. Taking the online market as an example, we examine the sources of the dividend difference and the factors influencing it with in a connectivity framework. We found that the narrowing of the access divide has resulted in enhanced connectivity and platform development, giving people the chance to benefit from transforming the various assets in which they have previously invested into differentiated compound connectivity capital. In the course, the scale and rate of the conversion are affected by two multiplier effects and especially by online platforms. The process is ultimately expressed in the dividend difference.