减排约束下中国天然气供需安全评价:2006-2014年

为准确把握中国天然气供需安全状态,以2006-2014年的数据为样本,在模糊隶属度函数量化指标值的基础上,运用层次分析法(AHP)和熵值法确定指标权重,基于线性加权和函数计算均方根进行综合评价。结果表明:(1)中国天然气供需安全指数呈现"N形"波动,2006-2007年天然气供需安全指数上升。2007-2013年天然气供需安全指数年均下降1.7979。2013年以来天然气供需安全指数开始回升,天然气供需安全压力有所缓解。(2)天然气供应安全指数从2006年的17.5914下降到2013年的0.1556,峰谷差17.4359。天然气需求安全指数呈现"W形"波动。天然气市场安全指数相对稳定,2013年以来略有上升,天然气市场风险逐年下降。(3)天然气供需安全很大程度上取决于天然气供应是否安全。因此,建议从确保天然气稳定供应、提高天然气使用效率和降低市场风险方面采取措施,维护天然气供需安全。     

A win ratio approach to comparing continuous non‐normal outcomes in clinical trials

Clinical trials are often designed to compare continuous non‐normal outcomes. The conventional statistical method for such a comparison is a non‐parametric Mann–Whitney test, which provides a P‐value for testing the hypothesis that the distributions of both treatment groups are identical, but does not provide a simple and straightforward estimate of treatment effect. For that, Hodges and Lehmann proposed estimating the shift parameter between two populations and its confidence interval (CI). However, such a shift parameter does not have a straightforward interpretation, and its CI contains zero in some cases when Mann–Whitney test produces a significant result. To overcome the aforementioned problems, we introduce the use of the win ratio for analysing such data. Patients in the new and control treatment are formed into all possible pairs. For each pair, the new treatment patient is labelled a ‘winner’ or a ‘loser’ if it is known who had the more favourable outcome. The win ratio is the total number of winners divided by the total numbers of losers. A 95% CI for the win ratio can be obtained using the bootstrap method. Statistical properties of the win ratio statistic are investigated using two real trial data sets and six simulation studies. Results show that the win ratio method has about the same power as the Mann–Whitney method. We recommend the use of the win ratio method for estimating the treatment effect (and CI) and the Mann–Whitney method for calculating the P‐value for comparing continuous non‐Normal outcomes when the amount of tied pairs is small. Copyright © 2016 John Wiley & Sons, Ltd.     

A new look at Markov processes of G/M/1-type

We present a new method for deriving the stationary distribution of an ergodic Markov process of G/M/1-type in continuous-time, by deriving and making use of a new representation for each element of the rate matrices contained in these distributions. This method can also be modified to derive the Laplace transform of each transition function associated with Markov processes of G/M/1-type.     

基于模糊理论评估连续梁桥地震破坏状况

为了研究不同强度等级地震作用下连续梁桥的破坏模式及破坏程度,采用多级模糊综合评判方法建立连续梁桥的综合评估模型。根据连续梁桥的特点,利用连续梁桥各单元及各构件的破坏程度建立多个因素集,根据各因素的权重和评价标准,对多个因素集进行逐级评判,根据最大隶属度原则得到地震作用下连续梁桥的破坏情况,并通过等级参数评判法对不同地震强度下连续梁桥的破坏情况进行等级评判,得到连续梁桥破坏情况与地震强度之间的关系。最后,基于多级模糊综合评判方法对一座连续梁桥实例进行评判     

基于改进突变级数法的农产品物流产业评估模型研究

对农产品物流产业发展水平进行客观、准确的分析与评估,可为政府部门优化与调整产业发展、合理制定产业规划提供科学依据。运用突变级数法与离差最大化法构建农产品物流产业评估模型,并选取广东和山东两省20102014年相关数据对构建的评估模型进行实例分析。结果表明：广东省农产品物流产业发展的优势主要体现在信息化水平与经济基础两方面,基础设施建设则是当前制约其产业发展的关键因素;而庞大的产业规模是现阶段支撑山东省农产品物流产业发展的重要力量。改进的突变级数评估模型既克服了主观赋权的局限,又不失科学性和合理性,计算简单,具有重要实用价值。     

未焊透缺陷安全尺寸的评价研究

未焊透是压力管道焊接接头常见缺陷之一,严重时会导致管道的承载能力明显降低,进而引发严重的安全事故。 目前我国的《在用工业管道定期检验规程》中对未焊透管道的定级是很保守的,为了减少对未焊透缺陷的返修,文章采 用非线性有限元分析的方法,通过简化未焊透管道的影响因素,并研究在不同载荷工况下管道失效模式与缺陷尺寸的关 系,获得了未焊透管道的极限载荷曲线簇;再运用塑性力学分析方法对有限元分析结果进行筛选,得到了焊接缺陷的安 全尺寸;最终通过试验验证极限载荷数值分析结果的合理性。研究结果表明含有超标焊接缺陷（如未焊透）的管道不通 过复杂应力计算和安全评定即可在保证安全前提下最大限度地使用。     

The k-factor GARMA Process with Infinite Variance Innovations

In this article, we develop the theory of k-factor Gegenbauer Autoregressive Moving Average (GARMA) process with infinite variance innovations which is a generalization of the stable seasonal fractional Autoregressive Integrated Moving Average (ARIMA) model introduced by Diongue et al. (2008 Diongue, A.K., Guégan, D. (2008). Estimation of k-Factor GIGARCH Process: A Monte Carlo Study. Communications in Statistics-Simulation and Computation 37:2037–2049.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Stationarity and invertibility conditions of this new model are derived. Conditional Sum of Squares (CSS) and Markov Chains Monte Carlo (MCMC) Whittle methods are investigated for parameter estimation. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. Finally, the usefulness of the model is corroborated with the application to streamflow data for Senegal River at Bakel.     

In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.