组合预测贝叶斯模型研究

评述了组合预测的基本思想和组合预测贝叶斯模型的研究现状,研究了多步贝叶斯信息更新情况下针对不同偏性特征的无偏组合预测模型,讨论了结合非样本信息和样本信息的组合权重贝叶斯更新模型,扩展了贝叶斯组合预测模型的现有成果,指出了进一步研究的方向．     

Evaluation of Atmospheric Transport Models for Use in Phase II of the Historical Public Exposures Studies at the Rocky Flats Plant

Five atmospheric transport models were evaluated for use in Phase II of the Historical Public Exposures Studies at the Rocky Flats Plant. Models included a simple straight-line Gaussian plume model (ISCST2), several integrated puff models (RATCHET, TRIAD, and INPUFF2), and a complex terrain model (TRAC). Evaluations were based on how well model predictions compared with sulfur hexafluoride tracer measurements taken in the vicinity of Rocky Flats in February 1991. Twelve separate tracer experiments were conducted, each lasting 9 hr and measured at 140 samplers in arcs 8 and 16 km from the release point at Rocky Flats. Four modeling objectives were defined based on the endpoints of the overall study: (1) the unpaired maximum hourly average concentration, (2) paired time-averaged concentration, (3) unpaired time-averaged concentration, and (4) arc-integrated concentration. Performance measures were used to evaluate models and focused on the geometric mean and standard deviation of the predicted-to-observed ratio and the correlation coefficient between predicted and observed concentrations. No one model consistently outperformed the others in all modeling objectives and performance measures. About 75% of the maximum hourly concentration predictions were within a factor of 5 of the observations. About 64% of the paired and 80% of the unpaired time-averaged model predictions were within a factor of 5 of the observations. The overall performance of the RATCHET model was somewhat better than the other models. All models appeared to experience difficulty defining plume trajectories, which was attributed to the influence of multilayered flow initiated by terrain complexities and the diurnal flow patterns characteristic of the Colorado Front Range.     

VaR风险度量模型在证券投资基金绩效评估中的应用

证券投资基金绩效评估的方法多种多样,现代理论认为,只有引进风险价值因素的绩效评估方法才能有效的对基金做出评价.因此,基金绩效评估的核心应该是对其所面临的风险进行准确的计算和测量.将VaR风险度量模型应用于证券投资基金绩效评估中,这种经风险调整后的绩效评估方法符合现代理论的要求,能更全面、有效的描述基金的真实收益.     

Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.     

Comparative Study of Predictive Computational Models for Nanoparticle‐Induced Cytotoxicity

With the increasing use of nanomaterials incorporated into consumer products, there is a need for developing approaches to establish “quantitative structure‐activity relationships” (QSARs). These relationships could be used to predict various biological responses after exposure to nanomaterials for the purposes of risk analysis. This risk analysis is applicable to manufacturers of nanomaterials in an effort to determine potential hazards. Because metal oxide materials are some of the most widely applicable and studied nanoparticle types for incorporation into cosmetics, food packaging, and paints and coatings, we focused on comparing different approaches for establishing QSARs for this class of materials. Metal oxide nanoparticles are believed, by some, to cause alterations in cellular function due to their size and/or surface area. Others have said that these nanomaterials, because of the oxidized state of the metal, do not induce stress in biological tests systems. This controversy highlights the need to systematically develop structure‐activity relationships (i.e., the relationship between physicochemical features to the cellular responses) and tools for predicting potential biological effects after a metal oxide nanomaterial exposure. Here, we attempt to identify a set of properties of two specific metal oxide nanomaterials—TiO₂ and ZnO—that could be used to characterize and predict the induced cellular membrane damage of immortalized human lung epithelial cells. We adopt a mathematical modeling approach that uses the engineered nanomaterial size characterized as a dry nanopowder and the nanomaterial behavior in ultrapure water, phosphate buffer, and cell culture media to predict nanomaterial‐induced cellular membrane damage (via lactate dehydrogenase release). Results of these studies provide insights on how engineered nanomaterial features influence cellular responses and thereby outline possible approaches for developing and applying predictive computational models for biological responses caused by exposure to nanomaterials.     

线性需求供应链中企业交叉持股的定价和绩效变化研究

供应链中企业交叉持股的定价和绩效的变化影响供应链上企业的合作关系乃至供应链竞争力的发挥。文章首先借助Stackelberg博弈模型讨论供应链中交叉持股对企业的影响,定量地推演无交叉持股前、交叉持股时和纵向一体化情况下定价和收益的差异;然后通过对推演结果的分析得出结论:交叉持股可以使市场价格下降的同时使供应链中企业的总收益增加;最后通过比较交叉持股与纵向一体化的外部效应,提出支持交叉持股、限制过度纵向一体化的企业战略建议。     

操纵性应计利润模型检测盈余管理能力的实证分析

本文运用统计模拟方法,通过测试各模型产生第一类错误和第二类错误的频率,比较基本琼斯模型、修正琼斯模型等七种常用的截面操纵性应计利润模型在中国资本市场的检验效果.检测结果发现,基本琼斯模型、修正琼斯模型和无形资产琼斯模型相对较优,它们所犯第一类错误和第二类错误的频率较小,但在收入操纵检测上,修正琼斯模型检验能力更突出;现金流量琼斯模型、非线性琼斯模型检验盈余管理的能力较强,但存在较为严重的第一类错误,易夸大上市公司盈余管理的程度;前瞻性修正琼斯模型计算复杂,第一类错误明显,检验盈余管理能力相对修正琼斯模型没有明显提高;收益匹配琼斯模型在费用操纵的检验能力上明显较弱.综合考虑,本文认为在中国证券市场上,分年度分行业回归的截面修正琼斯模型在模型的设定和盈余管理的检验能力方面表现更佳,建议未来的盈余管理实证研究以该方法为主.     

Logit模型在企业兼并市场绩效评价中的应用

本文采用后芝加哥学派对垄断的定义,设计了一个基于Logit模型的消费者需求函数,通过数值模拟的方法计算差异化产品企业兼并产生的市场绩效,最后用一组模拟数据对整个评价过程进行了检验。结果表明,一些导致市场集中度大幅上升的企业兼并,由于节约了边际成本,使兼并后企业的利润和竞争力大大提高,但并不会抬高相关市场的价格水平对消费者无害,因此不存在垄断效应。     

Framework for Evaluation of Physiologically-Based Pharmacokinetic Models for Use in Safety or Risk Assessment

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.     

企业知识管理绩效的模糊评价模型与分析矩阵

在总结国内外知识管理绩效评价指标体系研究现状的基础上,提出应从过程和结果2个方面对知识管理绩效进行评价;建立了一套实用性较强的知识管理评价指标体系,并尝试用"模糊多级综合评价方法"对企业的知识管理绩效进行评价;指出了对评价得分进行分析的方法,并独创了过程-结果分析矩阵.     

基于波动择时绩效的高维波动率估计量与预测模型研究

对协方差矩阵高频估计量和预测模型的选择,共同影响协方差的预测效果,从而影响波动择时投资组合策略的绩效。资产维数很高时,协方差矩阵高频估计量的构建会因非同步交易而丢弃大量数据,降低信息利用效率。鉴于此,将可以充分利用资产日内价格信息的KEM估计量用于估计中国股市资产的高维协方差矩阵,并与两种常用协方差矩阵估计量进行比较。进一步地,将三种估计量分别用于多元异质自回归模型、指数加权移动平均模型以及短、中、长期移动平均模型进行样本外预测,并比较在三种基于风险的投资组合策略下的经济效益。采用上证50指数中20只不同流动性成份股逐笔高频数据的实证研究发现:(1)无论是在市场平稳时期还是市场剧烈震荡期,长期移动平均模型都是高维协方差估计量预测建模的最优选择,在应用于各种波动择时策略时都可以实现最低成本和最高收益。(2)在市场平稳时期,KEM估计量是高维协方差估计的最优选择,应用于各种波动择时策略时基本都可以实现最低成本和最高收益;在市场剧烈震荡期,使用KEM估计量进行波动择时仍然可以在成本方面保持优势,但在收益上并不占优。(3)无论是在市场平稳时期还是市场剧烈震荡期,最低的成本都是在采用等风险贡献投...     

基于状态空间模型的动态基金业绩评价研究

本文在基金整体业绩评价研究领域对以往经典基金业绩评价指标詹森alpha指数以及基金资产投资的系统风险指标beta的估计方法进行了修正和改良。以往的詹森指数和beta值的估计是将其视为常系数,然而实际中基金的詹森指数和系统风险beta具备时变性。在对常系数下詹森alpha和系统风险beta值的分解式中,本文证明了传统估计值由詹森alpha和系统风险beta的期望值与一系列协方差项组成。随后本文构建了反映动态指标变化的SSM模型,并利用Particle EM算法来估计动态詹森alpha和系统风险beta在各期的估计值,并以此来计算基金在评价时期内平均詹森指数水平和系统风险水平。此外由于获取了各期的系统风险beta,根据择时能力的定义本文构建了反映基金在时期内的择时能力指标。     

Inference for the Identified Set in Partially Identified Econometric Models

This paper provides computationally intensive, yet feasible methods for inference in a very general class of partially identified econometric models. Let P denote the distribution of the observed data. The class of models we consider is defined by a population objective function Q(θ, P) for θ∈Θ. The point of departure from the classical extremum estimation framework is that it is not assumed that Q(θ, P) has a unique minimizer in the parameter space Θ. The goal may be either to draw inferences about some unknown point in the set of minimizers of the population objective function or to draw inferences about the set of minimizers itself. In this paper, the object of interest is Θ₀(P)=argmin_θ∈ΘQ(θ, P), and so we seek random sets that contain this set with at least some prespecified probability asymptotically. We also consider situations where the object of interest is the image of Θ₀(P) under a known function. Random sets that satisfy the desired coverage property are constructed under weak assumptions. Conditions are provided under which the confidence regions are asymptotically valid not only pointwise in P, but also uniformly in P. We illustrate the use of our methods with an empirical study of the impact of top‐coding outcomes on inferences about the parameters of a linear regression. Finally, a modest simulation study sheds some light on the finite‐sample behavior of our procedure.     

Accuracy of Simulations for Stochastic Dynamic Models

This paper is concerned with accuracy properties of simulations of approximate solutions for stochastic dynamic models. Our analysis rests upon a continuity property of invariant distributions and a generalized law of large numbers. We then show that the statistics generated by any sufficiently good numerical approximation are arbitrarily close to the set of expected values of the model's invariant distributions. Also, under a contractivity condition on the dynamics, we establish error bounds. These results are of further interest for the comparative study of stationary solutions and the estimation of structural dynamic models.     

Joint Models for Toxicology Studies with Dose-Dependent Number of Implantations

Many chemicals interfere with the natural reproductive processes in mammals. The chemicals may prevent the fertilization of an egg or keep a zygote from implanting in the uterine wall. For this reason, toxicology studies with pre-implantation exposure often exhibit a dose-related trend in the number of observed implantations per litter. Standard methods for analyzing developmental toxicology studies are conditioned on the number of implantations in the litter and therefore cannot estimate this effect of the chemical on the reproductive process. This article presents a joint modeling approach to estimating risk in toxicology studies with pre-implantation exposure. In the joint modeling approach, both the number of implanted fetuses and the outcome of each implanted fetus is modeled. Using this approach we show how to estimate the overall risk of a chemical that incorporates the risk of lost implantation due to pre-implantation exposure. Our approach has several distinct advantages over previous methods: (1) it is based on fitting a model for the observed data and, therefore, diagnostics of model fit and selection apply; (2) all assumptions are explicitly stated; and (3) it can be fit using standard software packages We illustrate our approach by analyzing a dominant lethal assay data set (Luning et al., 1966, Mutation Research, 3, 444-451) and compare ourresults with those of Rai and Van Ryzin (1985, Biometrics, 41,1-9) and Dunson (1998, Biometrics, 54, 558-569). In a simulation study, our approach has smaller bias and variance than the multiple imputation procedure of Dunson.     

股票市场流动性风险计量模型研究

本文通过对流动性风险本质属性的探讨,提出了目标流动性的概念,建立了两个新的流动性风险计量模型模型,一是用流动性不足的均值测度流动性风险模型;一是包含流动性不足及其波动性的流动性风险综合测度模型。并以上海证券交易所上市的148只A股为样本进行实证检验,结果表明该模型能够科学计量股票流动性风险。     

组织知识管理绩效的一种综合评价方法

针对如何评价知识管理绩效的问题,从知识管理的过程、组织结构和知识管理受益情况出发,分析了评价知识管理绩效应该考虑的主要因素,建立了一套包括知识管理的过程、组织结构、经济上的收益和效率的变化情况等四个不同方面的评价指标体系,并在此基础上,给出了一种知识管理绩效的综合评价方法。最后通过一个算例说明了给出方法的应用。     

上市公司董事会业绩评价模型

阐述了开发上市公司董事会业绩评价模型的意义,建立了业绩评价指标体系,并介绍了业绩评价模型实现的思想,描述了模糊综合评价系统的综合评价机理,最后给出了应用实例。应用实例表明,该模糊综合评价系统计算速度快、结果表达清楚、显示方式直观灵活。     

基于“发展”导向的企业绩效评价研究

针对适应市场经济的需要,本文提出了基于“发展”导向的企业评价理论与方法。首先,从企业评价理论发展和实践需要的角度,分析了建立基于“发展”导向的企业评价理论与方法的必要性,然后,以煤炭企业为例,从数量性、竞争力、有效性、风险性四个侧面构建了煤炭企业发展绩效评价的指标体系。针对煤炭企业发展依赖于其基础条件的天然性,呈现明显的“资源”与“区位”优势,而形成企业间的投入与产出“级差”的特点,提出其评价标准的确定应在对企业分类基础上进行,并依据科学性、动态性、激励性、公认性、可计量性等原则和各个指标的性质与特点,采用相应的方法确定了各个指标的评价标准。最后,建立了煤炭企业发展绩效综合评价模型。     

Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.