A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation‐based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source‐to‐source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.     

灾难报道中的政治传播及提升路径——以“马航事件”报道为例

作为灾难报道的马航失联报道，因现场缺席、多元报道形态、综合性等特征，成为中国政治传播的绝佳案例。本文以“马航事件”为例，分析了政治传播的国内外经验，提出其提升的四个基点：人本主义、有效性原则、新闻机制改革和政治修辞。     

Before,Now, and After: Assessing Hurricane Katrina Relief

We assess governmental and non-governmental responses to disasters using primary data of Hurricane Katrina survivors along the Mississippi Gulf Coast. Non-governmental sources include nonprofit relief groups, faith-based organizations, and survivors’ self-identified social networks. We assess the impact of these governmental and non-governmental relief efforts on survivors’ economic, psychological, physical, and social effects from the disaster. Our results show that social isolation significantly increases perceptions of disaster disturbance and decreases perceived rates of disaster relief. Additionally, survivors perceive that social networks provide greater sources of psychological, financial and social disaster relief than government sources. However, survivors’ social networks decay sharply in the immediate aftermath of a disaster, and they do not appear to fully recover a year from the disaster. These social networks themselves are not fully resilient to a disaster.

Nonparametrics: The Early Years—Impressions and Recollections

In 1942 Wolfowitz introduced the term nonparametric into the statistical literature to call attention to the need for extending then-existing statistical theory beyond the customary parametric framework. Subsequently, statistical methods that did not depend on a strictly parametric setup became known as nonparametric methods. This article surveys developments in nonparametrics roughly up to 1960. The suggestion is made that statistics might be better served by eliminating the term nonparametric altogether from the statistical vocabulary.     

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models

Summary.  We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.     

日本对自然灾害中住宅损坏的认定与救助政策

灾害救助不仅关乎人道而且可安定社会秩序。住宅是多数家庭的最大财产,而且是生活必需品。日本制定了自然灾害中住宅损坏鉴定制度,根据住宅损坏程度给予相应等级的援助。能够修理者给予免费应急修理;无法再住者则提供至多300万日元用于租房或者购建住宅、添置日用品。还有住宅重建的融资支援、各地方政府独自的援助措施等。调查显示目前的制度满意者多于不满者,约三分之二受灾家庭在重建住宅生活时无需借款。公共救助是纠正市场失误、于社会整体有益的制度。     

Extreme Risk Analysis of Interdependent Economic and Infrastructure Sectors

Willful attacks or natural disasters pose extreme risks to sectors of the economy. An extreme-event analysis extension is proposed for the Inoperability Input-Output Model (IIM) and the Dynamic IIM (DIIM), which are analytical methodologies for assessing the propagated consequences of initial disruptions to a set of sectors. The article discusses two major risk categories that the economy typically experiences following extreme events: (i) significant changes in consumption patterns due to lingering public fear and (ii) adjustments to the production outputs of the interdependent economic sectors that are necessary to match prevailing consumption levels during the recovery period. Probability distributions associated with changes in the consumption of directly affected sectors are generated based on trends, forecasts, and expert evidence to assess the expected losses of the economy. Analytical formulations are derived to quantify the extreme risks associated with a set of initially affected sectors. In addition, Monte Carlo simulation is used to handle the more complex calculations required for a larger set of sectors and general types of probability distributions. A two-sector example is provided at the end of the article to illustrate the proposed extreme risk model formulations.     

A joint model for longitudinal data profiles and associated event risks with application to a depression study

Summary.  In many longitudinal studies, a subject's response profile is closely associated with his or her risk of experiencing a related event. Examples of such event risks include recurrence of disease, relapse, drop-out and non-compliance. When evaluating the effect of a treatment, it is sometimes of interest to consider the joint process consisting of both the response and the risk of an associated event. Motivated by a prevention of depression study among patients with malignant melanoma, we examine a joint model that incorporates the risk of discontinuation into the analysis of serial depression measures. We present a maximum likelihood estimator for the mean response and event risk vectors. We test hypotheses about functions of mean depression and withdrawal risk profiles from our joint model, predict depression from updated patient histories, characterize associations between components of the joint process and estimate the probability that a patient's depression and risk of withdrawal exceed specified levels. We illustrate the application of our joint model by using the depression data.     

Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.     

Do all roads lead to Rome? Effect of educational trajectories on educational transitions

“Non-traditional” educational trajectories are increasingly common among American students. This study assesses the implications of this phenomenon for inequality in educational attainment. A proper account of educational trajectories requires simultaneous consideration of qualitatively different types of destinations within educational transitions, of the timing at which different transitions occur, and of the sequence of events within educational levels. To examine “traditional” and “non-traditional” pathways through post-secondary education, this study relies on detailed educational histories from the National Longitudinal Survey of Youth 1979–2002. Findings reveal that deviations from a traditional trajectory are widespread, are more frequent among students who enrolled in less selective colleges, and also among socioeconomically and academically disadvantaged students. Results show that following a “non-traditional” pathway reduces students’ chances to enroll in college and to complete a post-secondary degree. In the case of bachelor's degree completion, most of the observed gap among students from different socioeconomic backgrounds is accounted for the different trajectories students follow. This study demonstrates that a fine-grained analysis of students’ trajectories improves our understanding of the persistent socioeconomic disparities in educational attainment.