Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study

Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.     

In Memoriam: Emeritus Professor Henry Oliver Lancaster, AO FAA 1 February 1913 – 2 December 2001

The death, in Sydney, of Oliver Lancaster marks the end of an era in the histories of the Statistical Society of Australia, which (in its previous existence as the Statistical Society of New South Wales) he helped found in 1947, and of the Australian Journal of Statistics of which he was founding editor (1959–1971). Oliver Lancaster was Foundation Professor of Mathematical Statistics at the University of Sydney (1959–1978), where he spent his life as student and academic. During his academic career, he achieved scholarly distinction in at least four fields: mathematical statistics, medical and public health statistics, the history of medicine and of statistics, and statistical bibliography. With E.J.G. Pitman (1897–1993), M.H. Belz (1897–1975), E.A. Cornish (1909–1973) and P.A.P. Moran (1917–1988) he was part of a cohort of renowned Australian mathematical statisticians who laid the foundation of the glory days of Australian mathematical statistics. This obituary and tribute focuses on some of these aspects, within a broader historical picture.     

深度报道:历史性契约

关于“深度报道”的各种思考充斥于新闻传播学领域,遗憾的是,诸多的讨论却由于本体思考的缺失或倒错,最终偏离了期望的轨道,反造成知识上的混乱。究其实,“深度报道”是人类规避简单思维求取生存真相的一种努力。它一头扎进复杂的现实背景中去,该背景深入到事实的每一个原子中去,以至于特别的偏好事实的模糊性。它所追寻的是历史记忆的官能,它所采纳的是社会学的思考,它所贡献的是强烈的批判性。它是人类在“极速主义”时代求取清明的一种负责任的大努力。     

Expected sample moments of concomitants of selected order statistics

In this paper, the task of determining expected values of sample moments, where the sample members have been selected based on noisy information, is considered. This task is a recurring problem in the theory of evolution strategies. Exact expressions for expected values of sums of products of concomitants of selected order statistics are derived. Then, using Edgeworth and Cornish-Fisher approximations, explicit results that depend on coefficients that can be determined numerically are obtained. While the results are exact only for normal populations, it is shown experimentally that including skewness and kurtosis in the calculations can yield greatly improved results for other distributions.     

A relation for the covariances of order statistics from n independent and non-identically distributed random variables

We derive a simple relation satisfied by the covariances of order statistics in the i.i.d. case and then generalize it to the case when the variables are independent and non-identically distributed. This relation could be employed successfully either to check the calculations or to reduce the amount of direct computations involved in evaluating the covariances of order statistics from an outlier model.     

Uniform asymptotic linearity of a process based on a signed rank statistic

Consider the process with, cf. (1.2) on page 265 in B1, X₁, …, X_N a sample from a distribution F and, for i = 1, …, N, R${}_{\text{|x}{}_{\text{1}}\text{, - q}{}_{\text{1}}\text{ø|}}$ , the rank of |X₁ - q₁ø| among |X₁ - q₁ø|, …, |X_N - q_Nø|. It is shown that, under certain regularity conditions on F and on the constants p_i and q_i, T^ø_N(t) is asymptotically approximately a linear function of ø uniformly in t and in ø for |ø| ≤ C. The special case where the p_i and the q_i, are independent of i is considered.     

一种基于区间数判断矩阵的群决策新方法

针对基于区间数判断矩阵的群决策问题,提出了一种群决策新方法.借助于集值统计原理,构造了与区间数判断矩阵群信息等价的具有一定可靠度的确定数判断矩阵,建立了计算这类群信息集结值可靠度的模型,运用和积法对所得新判断矩阵进行处理,得到了一种简便的方案排序方法.最后,通过一个算例验证了该方法的有效性和实用性.     

Prediction intervals for future Weibull residual data

In reliability theory, risk analysis, renewal processes and actuarial studies, the residual lifetimes data play an important essential role in studying the conditional tail of the lifetime data. In this paper, based on some observed ordered residual Weibull data, we introduce different prediction methods for obtaining prediction intervals (PIs) of future residual lifetimes including likelihood, Wald, moments, parametric bootstrap, and highest conditional methods. Monte Carlo simulations are performed to compare the performances of the so obtained PIs and one data analysis is performed for illustration purposes.     

Hazard rate ordering of the second-order statistics from multiple-outlier PHR samples

In this paper, we compare the hazard rate functions of the second-order statistics arising from two sets of independent multiple-outlier proportional hazard rates (PHR) samples. It is proved that the submajorization order between the sample size vectors together with the supermajorization order between the hazard rate vectors imply the hazard rate ordering between the corresponding second-order statistics from multiple-outlier PHR random variables. The results established here provide theoretical guidance both for the winner's price for the bid in the second-price reverse auction in auction theory and fail-safe system design in reliability. Some numerical examples are also provided for illustration.