基于MDA机制的跨洋航空网时隙分配方法

考虑到飞机跨洋飞行的限制性条件,提出了一种改进的多单元双向拍卖机制来满足跨洋航空主干网的时隙分配的需要。该机制将VCG双向拍卖和传统的多单元双向拍卖进行结合和改进,并从激励相容、预算平衡、个人理性和机制效率等方面证明了该双向拍卖机制的性质。最后的仿真结果表明,该双向拍卖机制具有较高的效率。     

作物-环境信息的快速获取技术与传感仪器

作物-环境信息的快速获取是精准农业实施最为基本和关键的因素.作物信息主要包括作物生长过程中营养信息、生长发育不同阶段的生理信息和生态信息、呼吸作用、光合作用、根系发育等信息、病虫害信息等.环境信息主要包括土壤信息等.作物-环境信息具有数据海量、多维,空间分布差异大,时变性强等特点.传统的作物-环境信息获取方法耗时费力,无法满足精准农业信息快速获取的需要.为此需要大力研究适用于作物-环境信息快速获取的技术和传感仪器.目前主要采用的信息快速获取方法包括机器视觉、光谱分析以及多光谱与高光谱成像技术等.文中对目前主要开展的作物-环境信息快速获取技术和传感仪器进行了综述,并提出了进一步开展作物-环境信息快速获取技术和传感仪器的研究方向.     

信息系统中的信息粒与熵理论

信息粒度与熵理论是两种有效进行信息系统中不确定性研究的重要工具,已有许多成功的应用范例．文中研究了不同二元关系下信息系统中信息粒的刻画和表示,给出了信息系统中信息粒度的公理化定义,证实了一些已有的信息粒度度量都是其特殊形式．发展了信息系统中的熵理论,证明了这些熵度量的粒化单调性．同时,在信息系统意义下,建立了信息粒度与熵之间的互补关系．这些研究统一了完备信息系统与非完备信息系统中不确定性度量的相关结果．     

信息几何理论与应用研究进展

信息几何是在Riemann流形上采用现代微分几何方法来研究统计学问题的基础性、前沿性学科,被誉为是继Shannon开辟现代信息理论之后的又一新的理论变革,在信息科学与系统理论研究领域展现出了巨大的发展潜力．本文首先从参数化概率分布族的内蕴几何结构特征与信息的几何性质出发,精炼了信息几何的科学内涵,指出信息几何相比于经典统计学与信息论的理论优势与方法的革新．然后简要阐述了信息几何与微分几何的联系,综述了信息几何理论的发展历史与近20年来信息几何在神经网络、统计推断、通信编码、系统理论、物理学和医学成像等各领域应用的研究现状,归纳和总结了其中所体现的信息几何的基本原理和基本方法,并对信息几何的发展给予注记．特别地,对信息几何在信号处理领域中的应用成果进行了较全面的总结和概括,阐述了信息几何在信号检测、参数估计与滤波等方面的最新研究成果．最后,展望信息几何的发展前景,提出了信息几何在信号处理领域中的若干开放性问题．     

企业环境信息披露质量的影响因素研究

为了研究企业环境信息披露质量的影响因素,将传统会计信息质量的8个原则引入环境会计。建立环境信息质量评价体系后,选取2008～2012年污染行业180家上市公司为样本,从企业内部和外部两个方面对环境信息质量进行实证分析。分析结果显示,前十大股东的持股比例、独立董事比例、绿色认证以及公司的环保战略目标在0.01水平上与被解释变量显著相关。     

基于分组拍卖的集群机器人启发式图形构造算法

集群机器人的图形构造问题是指通过控制集群机器人的运动趋使其形成一个特定的图形.集群机器人中的图形构造问题通常可以分解为两个子问题:机器人与目标点之间的任务分配以及机器人与目标点之间的路径规划.根据集群机器人图形构造问题规模大、易拥堵、碰撞的特点,提出了一种集中优化、分组拍卖以及分布式交互相结合的OGADI(optimized grouping auction and distributed interaction)方法,以缩短图形构造的完成时间.将OGADI算法与最短路径集诱导顶点排序算法对比,结果表明,在集群机器人规模分别为500, 1000, 1300下, OGADI算法图形构造任务平均完成时间分别缩短了16.1%, 13.6%, 14.4%,仿真验证了OGADI算法的可行性和有效性.     

基于压缩感知思想的图像分块压缩与重构方法

压缩感知理论在数据获取、数据存储/传输、数据分析和处理方面有很大优势,成为近年来的研究热点.考虑到大多数图像信号信息分布有差异,编码端,在对图像分块的基础上,融合熵估计和边缘检测方法计算各图像块的信息含量,再从两个不同的角度进行分类采样:依据信息量多少将图像块分为平滑、过渡和纹理3类,使用不同的采样率采样;依据信息量的分布特征,采用不同的采样率分配策略进行采样.在解码端,根据不同类型的图像块构造不同的线性算子进行重构,再运用改进的迭代阈值算法去除块效应和噪声.实验证明,算法在提升图像重构质量的同时缩短了重构时间,并且对纹理边缘多的图像的重构效果较其他方法理想.     

网络环境下的信息对抗理论与技术

信息对抗是信息安全界最近才发展起来的一个研究领域其研究内容和内容和内涵人们还比较模糊，本文的主要目的是进一步明确这些问题。首先给出了研究信息对抗的背景和意义；其次，其于作者这对一研究领域的认识给出了信息对抗的定义，阐述了信息对抗的研究内容，并指出了每项研究中所解决的关键问题。     

水信息获取与处理技术及发展

在对水信息系统模型内涵与外延分析的基础上,综述了水信息获取与处理技术的研究与应用的内容：①水信息获取的基本理论框架;②水信息获取技术及发展;③多源信息融合处理技术。探讨了水信息系统的信息获取技术互补、信息融合算法选择和系统评估3个关键问题。     

独立董事任职经验对IPO企业经营绩效影响研究

基于对独立董事和企业经营绩效相关文献的研究,探索独立董事的任职经验对IPO企业经营绩效的影响。采用CSMAR数据库中2002~2013年披露了招股说明书的1059家IPO企业中有关独立董事的信息,得到其在IPO之前曾经在其他上市企业或IPO企业的任职经验、席位数量。根据多元线性回归的实证研究方法发现,有其他上市企业IPO经验的独立董事能够有效提高企业上市后的经营绩效,并有效抑制企业上市之前的盈余管理水平,提高企业会计信息质量。     

Modeling and Forecasting Online Auction Prices: A Semiparametric Regression Analysis

Interest in online auctions has been growing in recent years. There is an extensive literature on this topic, whereas modeling online auction price process constitutes one of the most active research areas. Most of the research, however, only focuses on modeling price curves, ignoring the bidding process. In this paper, a semiparametric regression model is proposed to model the online auction process. This model captures two main features of online auction data: changing arrival rates of bidding processes and changing dynamics of prices. A new inference procedure using B‐splines is also established for parameter estimation. The proposed model is used to forecast the price of an online auction. The advantage of this proposed approach is that the price can be forecast dynamically and the prediction can be updated according to newly arriving information. The model is applied to Xbox data with satisfactory forecasting properties. Copyright © 2016 John Wiley & Sons, Ltd.     

Modeling eBay price using stochastic differential equations

Online auctions have become increasingly popular in recent years. There is a growing body of research on this topic, whereas modeling online auction price curves constitutes one of the most interesting problems. Most research treats price curves as deterministic functions, which ignores the random effects of external and internal factors. To account for the randomness, a more realistic model using stochastic differential equations is proposed in this paper. The online auction price is modeled by a stochastic differential equation in which the deterministic part is equivalent to the second‐order differential equation model proposed in Wang et al. (Journal of the American Statistical Association, 2008, 103, 1100–1118). The model also includes a component representing the measurement errors. Explicit expressions for the likelihood function are also obtained, from which statistical inference can be conducted. Forecast accuracy of the proposed model is compared with the ODE (ordinary differential equation) approach. Simulation results show that the proposed model performs better.     

Step-ahead spot price densities using daily synchronously reported prices and wind forecasts

This paper uses non-linear methodologies to follow the synchronously reported relationship between the Nordic/Baltic electric daily spot auction prices and geographical relevant wind forecasts in MWh from early 2013 to 2020. It is a well-known market (auctions) microstructure fact that the daily wind forecasts are information available to the market before the daily auction bid deadline at 11 a.m. The main objective is therefore to establish conditional and marginal step ahead spot price density forecast using a stochastic representation of the lagged, synchronously reported and stationary spot price and wind forecast movements. Using an upward expansion path applying the Schwarz (Bayesian information criterion [BIC]) criterion and a battery of residual test statistics, an optimal maximum likelihood process density is suggested. The optimal specification reports a significant negative covariance between the daily price and wind forecast movements. Conditional on bivariate lags from the SNP information and using the known market information for wind forecast movements at t₁, the paper establishes one-step-ahead bivariate and marginal day-ahead spot price movement densities. The result shows that wind forecasts significantly influence the synchronously reported spot price densities (means and volatilities). The paper reports day-ahead bivariate and marginal densities for spot price movements conditional on several very plausible price and wind forecast movements. The paper suggests day-ahead spot price predictions from conditional and synchronously reported wind forecasts movements. The information should increase market participants spot market insight and consequently make spot price predictions more accurate and the confidence interval considerably narrower.     

Fiscal transparency,fiscal forecasting and budget credibility in developing countries

Fiscal transparency entails fiscal information disclosure, ensuring good quality of such information, public and parliamentary scrutiny, and the oversight of fiscal risks, while the concept of budget credibility involves an assessment of the extent of conformity of budgetary outturn to the planned forecasts. There are two main channels through which fiscal transparency can affect budget credibility: politicians' incentives to manipulate fiscal forecasts or deviate from the plans, and the uncertainty‐reducing effect in the light of fiscal risks. This study empirically examines the impact of the dimensions of fiscal transparency on deviations from budgetary forecasts, and hence budget credibility, in developing countries using regression analysis. The results show that an improvement in the oversight of fiscal risks arising from public sector entities plays a very significant role in decreasing the deviations from budgetary forecasts in developing countries. The study accords with the view that a relatively less important weight should be attached to the fiscal disclosure component in analyzing fiscal transparency, and it sheds light on the importance of ensuring good quality of disclosed information through more independence of audit bodies in developing countries in order to enhance the credibility of their budgets.     

Forecasting Online Auctions via Self‐Exciting Point Processes

Modeling online auction prices is a popular research topic among statisticians and marketing analysts. Recent research mainly focuses on two directions: one is the functional data analysis (FDA) approach, in which the price–time relationship is modeled by a smooth curve, and the other is the point process approach, which directly models the arrival process of bidders and bids. In this paper, a novel model for the bid arrival process using a self‐exciting point process (SEPP) is proposed and applied to forecast auction prices. The FDA and point process approaches are linked together by using functional data analysis technique to describe the intensity of the bid arrival point process. Using the SEPP to model the bid arrival process, many stylized facts in online auction data can be captured. We also develop a simulation‐based forecasting procedure using the estimated SEPP intensity and historical bidding increment. In particular, prediction interval for the terminal price of merchandise can be constructed. Applications to eBay auction data of Harry Potter books and Microsoft Xbox show that the SEPP model provides more accurate and more informative forecasting results than traditional methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Application of the linear partial information model to forecasting the Swiss timber market

Most often, statistical analyses only provide partial information about the appropriateness of different models, structures and parameters which may underlie the dynamic process that has generated a time series. Linear partial information (LPI), in particular, consists of linear restrictions such as LPI: p_a> p_b, p_a> p_c where p_a denotes the probability that structure a holds. Fuzzy information of this type can be put to use for decision-making by LPI analysis. In this paper, LPI analysis is applied to answer the question of whether subsidizing price, given an abnormal disturbance on the timber market, would contribute to continuous forest management, a stated goal of Swiss environmental policy.     

带信息反馈的凝聚层次聚类算法

本文针对传统的基于相似性的层次聚类算法存在的两个问题(相似性度量中方向信息的丢失和算法的适应能力弱)提出了一种带有信息反馈的凝聚层次聚类算法.首先将无法预知的复杂数据结构描述成3个基本的结构特征单元,并对其进行建模构建一种相似性度量定义的泛型和一种凝聚的层次聚类算法.在凝聚的层次聚类算法中加入类信息的反馈机制,并在不同阶段对相似性定义的泛型进行具体化,充分利用数据点对之间的方向信息和距离信息进行聚类.该聚类算法主要有两大优势:(i)算法的适应能力较强,不需要假设的前提下可以处理无法预知的复杂数据结构;(ii)算法对噪声具有较强的鲁棒性,在不需要对数据集进行预处理的情况下能够在聚类的过程中识别噪声点或者噪声类.从人工数据和真实数据的试验结果可以看出新算法的优越性能.     

Extraneous expert information

When a Bayesian decision maker has to choose among information sources, he should consider the anticipated impact that the information will have on his posterior distribution. In some cases he may determine at the outset that an information source will have no effect on his posterior beliefs, no matter what that source says. Such an information source is called extraneous. In this paper we discuss Bayesian conditions for extraneous information sources, and show a hypothetical example involving experts with overlapping information. Analysis of U.S. weather forecasts demonstrates how this concept can be operationalized to test hypotheses concerning the use of information by forecasters.