目前遗传资源和传统知识法律保护机制国际探索的成就与不足——评CBD事先知情同意机制和FAO农民权机制

传统知识是长期以来传统部族在长期的生产生活实践中创造出来的知识、技术、经验的总称,是一种知识形态和知识产品。CBD等国际组织对遗传资源及有关传统知识的保护进行了多年的探索,在保护机制上取得了一定的创新。CBD开创的事先知情同意机制和FAO开创的农民权机制为其代表。两种机制均在物权层面设计了遗传资源及有关传统知识的保护规则,但前者具有较强的保障力,后者基本上还是一种道义意义上的权利。但只有提供知识产权保护方能充分实现传统部族在传统知识上的利益。     

Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data

In this paper, we consider parametric Bayesian inference for stochastic differential equations driven by a pure‐jump stable Lévy process, which is observed at high frequency. In most cases of practical interest, the likelihood function is not available; hence, we use a quasi‐likelihood and place an associated prior on the unknown parameters. It is shown under regularity conditions that there is a Bernstein–von Mises theorem associated to the posterior. We then develop a Markov chain Monte Carlo algorithm for Bayesian inference, and assisted with theoretical results, we show how to scale Metropolis–Hastings proposals when the frequency of the data grows, in order to prevent the acceptance ratio from going to zero in the large data limit. Our algorithm is presented on numerical examples that help verify our theoretical findings.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

Letter to the editor comments on Groparu-Cojocaru and Doray (2013)

Although estimating the five parameters of an unknown Generalized Normal Laplace (GNL) density by minimizing the distance between the empirical and true characteristic functions seems appealing, the approach cannot be advocated in practice. This conclusion is based on extensive numerical simulations in which a fast minimization procedure delivers deceiving estimators with values that are quite far away from the truth. These findings can be predicted by the very large values obtained for the true asymptotic variances of the estimators of the five parameters of the true GNL density.     

Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non-OECD Countries with Focus on Time Trend Analysis

This study presents probabilistic analysis of dam accidents worldwide in the period 1911–2016. The accidents are classified by the dam purpose and by the country cluster, where they occurred, distinguishing between the countries of the Organization for Economic Cooperation and Development (OECD) and nonmember countries (non-OECD without China). A Bayesian hierarchical approach is used to model distributions of frequency and severity for accidents. This approach treats accident data as a multilevel system with subsets sharing specific characteristics. To model accident probabilities for a particular dam characteristic, this approach samples data from the entire data set, borrowing the strength across data set and enabling to model distributions even for subsets with scarce data. The modelled frequencies and severities are combined in frequency-consequence curves, showing that accidents for all dam purposes are more frequent in non-OECD (without China) and their maximum consequences are larger than in OECD countries. Multipurpose dams also have higher frequencies and maximum consequences than single-purpose dams. In addition, the developed methodology explicitly models time dependence to identify trends in accident frequencies over the analyzed period. Downward trends are found for almost all dam purposes confirming that technological development and implementation of safety measures are likely to have a positive impact on dam safety. The results of the analysis provide insights for dam risk management and decision-making processes by identifying key risk factors related to country groups and dam purposes as well as changes over time.     

应用混合神经网络和遗传算法的期权价格预测模型

隐含波动率是指在市场中观察的期权价格所蕴涵的波动率.提出了一种加权的隐含波动率作为混合神经网络的输入变量,建立了混合神经网络和遗传算法相结合的期权价格预测模型,通过遗传算法来优化神经网络的结构和获得隐含波动率的权重.在对香港金融衍生品市场的实证中表明,本文模型在预测结果上要优于传统的Black-Scholes模型.     

通货膨胀下的联合库存选址模型研究

研究了通货膨胀对涉及一个供应商、多个候选配送中心和零售商的三级分销网络设计的影响.不同于传统的联合库存选址模型,所研究的模型考虑了整个系统的相关成本的时间价值,其目标是使在一定的通货膨胀率下总的选址、运输和库存成本之和的现值最小.为此,建立了一个非线性的整数规划模型,该模型为无容量约束问题的扩展模型,属于NP-hard问题.提出了3种基于不同编码形式的遗传算法用于求解该模型,并通过不同规模的算例对3种算法进行了有效性的分析和对比.最后,通过对各算例的计算结果的分析,发现随着通货膨胀率的增长,为了使这个分析网络的总成本现值最小,需要增加建立的DC的数量.     

遗传算法优化神经网络及信用评价研究

研究关于公司神经网络信用评估问题的现状,提出遗传算法辅助网络训练策略(优化后的网络称为进化网络),克服传统网络建模中产生的局部极小缺陷。建立了适合于我国商业企业的信用评分指标体系;然后依据该指标体系建立了基于进化神经网络的信用评估模型;最后,利用样本公司实际指标数据对该模型的评分效果进行了比较研究。     

两级供应链Stackelberg主从对策的优化模型及其应用

本文研究了供应链的协调订货模式及其特例,提出了两级供应链Stackelberg主从对策问题模型,其中卖方作为主方给出最小补充期策略,买方作为从方以最优库存策略响应。建立了求解Stackelberg主从对策问题的成本优化模型,并考虑了累积价格折扣激励和买方库存成本合理化。对一个石油分销系统Stackelberg主从对策问题应用遗传算法离线仿真计算,得出Stackelberg均衡解,达到帕累托最优。     

Combining Food Frequency and Survey Data to Quantify Long-Term Dietary Exposure: A Methyl Mercury Case Study

Twenty-four-hour recall data from the Continuing Survey of Food Intake by Individuals (CSFII) are frequently used to estimate dietary exposure for risk assessment. Food frequency questionnaires are traditional instruments of epidemiological research; however, their application in dietary exposure and risk assessment has been limited. This article presents a probabilistic method of bridging the National Health and Nutrition Examination Survey (NHANES) food frequency and the CSFII data to estimate longitudinal (usual) intake, using a case study of seafood mercury exposures for two population subgroups (females 16 to 49 years and children 1 to 5 years). Two hundred forty-nine CSFII food codes were mapped into 28 NHANES fish/shellfish categories. FDA and state/local seafood mercury data were used. A uniform distribution with minimum and maximum blood-diet ratios of 0.66 to 1.07 was assumed. A probabilistic assessment was conducted to estimate distributions of individual 30-day average daily fish/shellfish intakes, methyl mercury exposure, and blood levels. The upper percentile estimates of fish and shellfish intakes based on the 30-day daily averages were lower than those based on two- and three-day daily averages. These results support previous findings that distributions of "usual" intakes based on a small number of consumption days provide overestimates in the upper percentiles. About 10% of the females (16 to 49 years) and children (1 to 5 years) may be exposed to mercury levels above the EPA's RfD. The predicted 75th and 90th percentile blood mercury levels for the females in the 16-to-49-year group were similar to those reported by NHANES. The predicted 90th percentile blood mercury levels for children in the 1-to-5-year subgroup was similar to NHANES and the 75th percentile estimates were slightly above the NHANES.