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.     

大数据时代国防动员潜力调查之路

如何应用大数据提升国防动员潜力调查的效率是当前政府重点关注的话题。从国防动员潜力大数据的定义和特征着手,分析大数据下国防动员潜力调查所具备的优势,发现仍然存在思维局限、指标不统一、体制不健全、技术缺乏及数据共享与防护难协同等困境。通过思维意识、管理体制、指标体系、核心技术、专业人才、安全共享等六大模块,打破“数据不可用、数据不会用”的现实瓶颈,实现整合国防动员数据资源、延展数据内容和提高工作效率的目标。     

大学化学实验课程中有效数字的记录

有效数字是大学化学实验课程的主要教学内容之一,是培养学生在课程中理解并在日常学习与工作中规范使用有效数字,提升其对有效数字与测定误差之间关系的认识水平,树立良好规范的数字记录习惯,具有十分重要的现实意义。论述介绍了目前大学化学实验教学中关于有效数字方面存在的主要问题,并采用相应的举措建立和完善了教学方案以利于更好地解决存在的问题。通过几个学期的教学实践发现新教学方案取得了初步的成效,同时严格按照有效数字的要求记录与计算数据也是培养学生具有严谨科学素养的一条重要途径。     

Spatial Data Usage,Spatial Thinking and Spatial Knowledge Generation: The Case of Planning Practitioners in Arequipa,Peru

Spatial thinking and spatial knowledge generation in decision-making are still not mature fields of study in planning research, despite these being crucial elements in addressing the issues of the twenty-first-century city. This article contributes to their understanding by exploring their interrelationships with spatial data usage. Through storylines, it analyzes the arguments that planning practitioners offer in support of infrastructure-led decisions in Arequipa (Peru), before and after spatial data usage. The article concludes that spatial data usage improve spatial thinking to different extents, yet suggests aligning spatial data generation and the inclusion of GIS-based spatial analyses with the spatial knowledge needed by routine planning practice.     

Simultaneous Confidence Intervals for the Population Cell Means,for Two-by-Two Factorial Data,that Utilize Uncertain Prior Information

Consider a two-by-two factorial experiment with more than one replicate. Suppose that we have uncertain prior information that the two-factor interaction is zero. We describe new simultaneous frequentist confidence intervals for the four population cell means, with simultaneous confidence coefficient 1 ? α, that utilize this prior information in the following sense. These simultaneous confidence intervals define a cube with expected volume that (a) is relatively small when the two-factor interaction is zero and (b) has maximum value that is not too large. Also, these intervals coincide with the standard simultaneous confidence intervals obtained by Tukey’s method, with simultaneous confidence coefficient 1 ? α, when the data strongly contradict the prior information that the two-factor interaction is zero. We illustrate the application of these new simultaneous confidence intervals to a real data set.     

Nonparametric analysis of treatment effects with missing observations

This paper develops a test for comparing treatment effects when observations are missing at random for repeated measures data on independent subjects. It is assumed that missingness at any occasion follows a Bernoulli distribution. It is shown that the distribution of the vector of linear rank statistics depends on the unknown parameters of the probability law that governs missingness, which is absent in the existing conditional methods employing rank statistics. This dependence is through the variance–covariance matrix of the vector of linear ranks. The test statistic is a quadratic form in the linear rank statistics when the variance–covariance matrix is estimated. The limiting distribution of the test statistic is derived under the null hypothesis. Several methods of estimating the unknown components of the variance–covariance matrix are considered. The estimate that produces stable empirical Type I error rate while maintaining the highest power among the competing tests is recommended for implementation in practice. Simulation studies are also presented to show the advantage of the proposed test over other rank-based tests that do not account for the randomness in the missing data pattern. Our method is shown to have the highest power while also maintaining near-nominal Type I error rates. Our results clearly illustrate that even for an ignorable missingness mechanism, the randomness in the pattern of missingness cannot be ignored. A real data example is presented to highlight the effectiveness of the proposed method.     

救赎：寻求民族文化的出路——解读莫里森的《柏油孩子》

高速铁路具有高度集成、高精度的技术特点，运营过程中经受列车质量、速度、密度等多种因素影响，地理位置因素往往是影响铁路设备状态演变的决定性因素。采用网格化管理技术可将空间上连续分布的管理对象划分成较小的单元网格，有利于从空间位置角度研究管理对象状态的变化规律。随着信息系统技术、大数据技术的迅猛发展，基于位置而不是基于专业更符合高速铁路的管理需求，网格化管理技术给高速铁路管理带来了新的视角。     

Asymptotic relative efficiency of the linear discriminant function under partial nonrandom classification of the training data

This paper considers the problem where the linear discriminant rule is formed from training data that are only partially classified with respect to the two groups of origin. A further complication is that the data of unknown origin do not constitute an observed random sample from a mixture of the two under- lying groups. Under the assumption of a homoscedastic normal model, the overall error rate of the sample linear discriminant rule formed by maximum likelihood from the partially classified training data is derived up to and including terms of the first order in the case of univariate feature data. This first- order expansion of the sample rule so formed is used to define its asymptotic efficiency relative to the rule formed from a completely classified random training set and also to the rule formed from a completely unclassified random set.     

Identifying Skeleton Curves in Noisy Data

This article presents a Bayesian method to reconstruct the centerline in noisy data using B-spline curves. The method is illustrated on simulated two- and three-dimensional data and is applied to recover the centerline of the colon in single photon emission computed tomography images.     

Robust block bootstrap panel predictability tests*

This article develops two block bootstrap-based panel predictability test procedures that are valid under very general conditions. Some of the allowable features include cross-sectional dependence, heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity. While the first test procedure tests if there is any predictability at all, the second procedure determines the units for which predictability holds in case of a rejection by the first. A weak unit root framework is adopted to allow persistent predictors, and a novel theory is developed to establish asymptotic validity of the proposed bootstrap. Simulations are used to evaluate the performance of our tests in small samples, and their implementation is illustrated through an empirical application to stock returns.