Scale invariance of incident size distributions in response to sizes of their causes.

Incidents can be defined as low-probability, high-consequence events and lesser events of the same type. Lack of data on extremely large incidents makes it difficult to determine distributions of incident size that reflect such disasters, even though they represent the great majority of total losses. If the form of the incident size distribution can be determined, then predictive Bayesian methods can be used to assess incident risks from limited available information. Moreover, incident size distributions have generally been observed to have scale invariant, or power law, distributions over broad ranges. Scale invariance in the distributions of sizes of outcomes of complex dynamical systems has been explained based on mechanistic models of natural and built systems, such as models of self-organized criticality. In this article, scale invariance is shown to result also as the maximum Shannon entropy distribution of incident sizes arising as the product of arbitrary functions of cause sizes. Entropy is shown by simulation and derivation to be maximized as a result of dependence, diversity, abundance, and entropy of multiplicative cause sizes. The result represents an information-theoretic explanation of invariance, parallel to those of mechanistic models. For example, distributions of incident size resulting from 30 partially dependent causes are shown to be scale invariant over several orders of magnitude. Empirical validation of power law distributions of incident size is reviewed, and the Pareto (power law) distribution is validated against oil spill, hurricane, and insurance data. The applicability of the Pareto distribution, in particular, for assessment of total losses over a planning period is discussed. Results justify the use of an analytical, predictive Bayesian version of the Pareto distribution, derived previously, to assess incident risk from available data.     

Predicting Pareto and exponential observables

The author considers, from a Bayesian viewpoint, the problem of predicting a future fraction of observables that lie in a set when the present and future observables are Pareto-distributed. Exact solutions as well as convenient approximations are obtained for the censored case.     

基于序数信息的博弈理论

在许多社会或政治科学的问题中, 个人的主观偏好信息往往是通过定性分析得到的, 难 以表示成效用形式, 但却可以用序数形式来表示. 本文基于决策者对策略的偏好序数信息而不 是支付函数或效用水平, 讨论了一种新的博弈理论或模型, 给出了若干序均衡的概念, 并进行 了例示分析. 这种基于序数信息的博弈理论可以看作是现有基于支付函数博弈理论的推广和 补充.     

中国房地产业与银行业动态相关性及风险溢出性——基于GPD-Copula-CoVaR模型的实证研究

文章通过构建GPD-Copula-CoVaR模型,考察了2000年1月至2017年6月中国房地产与银行业的动态相关性与风险溢出性,并通过Monte Carlo方法拟合VaR,检验了模型样本外的预测能力。结果表明,房地产与银行的相关性与房地产市场的繁荣及政策调控密切相关,但严厉的政策调控只能暂时降低两个行业的相关性,并不是长效机制;样本外预测显示GPD-BB1Copula模型对实际风险损失的覆盖率更高,模型预测效果更好;此外,房地产与银行存在双向风险溢出效应,其中房地产对银行的风险溢出更强,约为40%。研究对相关投资者和政策制定者具有较强的应用价值。     

An Adaptive Efficient Test for Gumbel Domain of Attraction

We consider n independent observations, generated identically by some distribution function, which belongs to the domain of attraction of an extreme value distribution with unknown shape and scale parameter. We treat the scale parameter as a nuisance parameter and establish an adaptive efficient test sequence, which is based on the k _n largest observations, for the Gumbel domain of attraction. Efficiency is achieved along certain contiguous extreme value alternatives within the concept of local asymptotic normality (LAN). Simulations exemplify the results     

Statistical Inference for a New Class of Multivariate Pareto Distributions

Various solutions to the parameter estimation problem of a recently introduced multivariate Pareto distribution are developed and exemplified numerically. Namely, a density of the aforementioned multivariate Pareto distribution with respect to a dominating measure, rather than the corresponding Lebesgue measure, is specified and then employed to investigate the maximum likelihood estimation (MLE) approach. Also, in an attempt to fully enjoy the common shock origins of the multivariate model of interest, an adapted variant of the expectation-maximization (EM) algorithm is formulated and studied. The method of moments is discussed as a convenient way to obtain starting values for the numerical optimization procedures associated with the MLE and EM methods.     

Enhancement of sigma level in the manufacturing of furnace nozzle through DMAIC approach of Six Sigma: a case study

In this paper, a case study on implementing Define, Measure, Analyze, Improve and Control (DMAIC) phases of Six Sigma programme in a furnace manufacturing company is reported. When Six Sigma was developed at Motorola, it was encapsulated with DMAIC and belt-based training infrastructure. Since belt-based training is expensive, companies with less revenue have been implementing only DMAIC phases. In this background, the impact of implementing DMAIC phases was examined in the research narrated in this paper through the reporting of the case study mentioned above. As the result of implementing DMAIC phases, the sigma level increased from 3.31 to 3.67 in the case of drilling a hole in a component called ‘furnace nozzle’. This increase in sigma level proved that DMAIC phases are powerful enough to enable the companies with less revenue to perform at six sigma level quality and acquire competitive strength.     

Optimal bounds for the mean of the total time on test for distributions with decreasing generalized failure rate

During any life test experiment it is of interest to study potential costs (or profits) of performing the test to the very end. Assuming that these costs are proportional to lifetimes of analysed items the experimenter needs to know the remaining total time on test, having just observed successive failure in the test. We derive sharp upper bounds on the expectation of the remaining total time on test statistic when the underlying distributions have decreasing generalized failure rate with respect to generalized Pareto distributions. In particular we obtain the bounds valid for distributions with decreasing density or failure rate. The results are illustrated with numerical examples.     

约束空间下生长曲线模型的相对效率

对于生长曲线模型中未知参数矩阵 B 的 LSE 与 BLUE 的相对效率,在空间受约束的条件下,改进了它的下界。     

绿色农业概念的经济学审视

绿色农业是指一种有利于环境保护,有利于农产品数量与质量安全的现代农业发展的形态与模式.本文对国内已有绿色农业概念进行了综述,分析众多概念中的优点和不足之处,提出绿色农业并不是一种全新的农业发展模式,而是社会需求拉动下农业功能逐步优化与高级化的过程,并运用经济学中的恩格尔曲线、帕累托效率及柠檬市场理论对绿色农业的概念进行了推导.