bluuebunny/crossref_metadata_2025_split huggingface dataset:
Causal Diagrams
Authors: Greenland Sander , Pearl Judea 
Published: September 2014

AbstractFrom their inception, causal systems models (more commonly known asstructural‐equations models) have been accompanied by graphical representations or path diagrams that provide compact summaries of qualitative assumptions made by the models. These diagrams can be reinterpreted as probability models, enabling use of graph theory in probabilistic inference, and allowing easy deduction of independence conditions implied by the assumptions. They can also be used as a formal tool for causal inference, such as predicting the effects of external interventions. Given that the diagram is correct, one can see whether the causal effects of interest (target effects, or causal estimands) can be estimated from available data, or what additional observations are needed to validly estimate those effects. One can also see how to represent the effects as familiar standardized effect measures. The present article gives an overview of the following: (a) components of causal graph theory; (b) probability interpretations of graphical models; and (c) methodological implications of the causal and probability structures encoded in the graph, such as sources of bias and the data needed for their control.
Health Hazards Posed by Dioxin
Authors: Paustenbach Dennis J. 
Published: September 2014

AbstractThis article reviews the scientific evidence and methodologies that have been used to assess the human health risks posed by 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin (TCDD). It also presents a probabilistic analysis for identifying virtually safe concentrations of TCDD toxicity equivalents (TEQ) in residential soils. Updated data distributions that consider state‐of‐the‐science cancer and noncancer toxicity criteria, child soil ingestion and dermal uptake, bioavailability in soil, and residential exposure duration are incorporated.
Early Warning Systems (EWSs) for Predicting Financial Crisis
Authors: Yung Sunny W.S. 
Published: September 2014

AbstractResearch on the development of early warning systems (EWSs) for predicting financial crises has gathered momentum in the 1990s. EWS provides a systematic, consistent, and unbiased means to process and analyze information pertinent to financial crises. EWS build on the premise that an economy and its financial markets would behave differently prior to an imminent financial crisis. The “abnormal” behavior has a systematic and recurrent pattern. Therefore, one can judge whether a crisis is about to occur, from the movements of relevant economic and financial indicators. Recent research efforts on EWS have been focusing on currency crises. The two most extensive and well‐documented approaches are the signal approach and the limited dependent regression approach. While EWS may not be sufficiently robust to be relied upon as the sole tool to predict financial crises, they perform substantially better than nonmodel‐based forecasts in the prediction of the Asian financial crises in the late 1990s. Development in other research frameworks that are complementary to EWS is encouraging. One such effort is the resilience framework that assesses the soundness of economic and financial systems at a particular point of time rather than predicting the occurrence of financial crises.
Professional Organizations in Risk Management
Authors: Apostolik Richard 
Published: September 2014

Abstract
The role of professional organizations in risk management is not a new concept. Many have been around for 50 or more years meeting the specialized needs of their members. What is new is the increasingly important role these organizations are now playing in assisting in the ongoing personal education of financial professionals and the development of the global regulatory environment within the financial services industry. This article explores the rationale behind the proliferation of professional organizations in the field of financial risk management, their historical role, what financial risk‐management professionals expect from these organizations, what the organizations can be reasonably expected to deliver, and the future role of professional risk‐management organizations of which the rapidly growing global discipline of financial risk management is a part.
Two basic issues need to be addressed before dealing with the expectations for and the delivery of products and services from a professional risk‐management association. These issues present somewhat of a “chicken‐and‐egg” problem. The first involves defining financial risk management, and the second relates to simply being able to define the organization's role in attempting to realistically meet the expectations of an increasingly broad number of varied constituencies in which probably the most dynamic discipline is the financial services industry.
Operational Risk Modeling
Authors: Cruz Marcelo 
Published: September 2014

Abstract
Modeling operational risk is a key challenge for the financial industry particularly after the Basel Committee issued the New Capital Accord in June, 2004 that established a capital charge for this risk for the first time. Although there are three methods for risk measurement allowed by the regulators (the basic indicator, the standardized approach, and the advanced measurement approach), most large institutions are going in for the advanced approach, which implies the development of their own framework for risk measurement and capital allocation. Despite the fact that the New Capital Accord was very strict on the rules for the data inputs to the model, it was also very broad in terms of which model should be used for measuring operational risk. In this article, the concept of modeling an operational risk database is explained and a framework based on statistical and mathematical approaches to measure and manage this risk is delineated.
Cyber Risk Management
Authors: Wallner James 
Published: September 2014

AbstractIn most modern information systems (IS) and process control systems (PCS), functionality and security are competing design goals. Therefore, system designers and operators are constantly forced to make security‐related trade‐off decisions. To reach the right balance between functionality and security, systems security engineers and network operators have to build information and process control systems that are secure against real‐world cyber attacks without overengineering against any particular one. By understanding which cyber attacks are most likely and which risks are most serious, system designers and operators can make informed security‐related trade‐off decisions.Cyber risk managementis the process of studying the threats, vulnerabilities, and mission impacts of cyber attacks that provides the appropriate metric for trading off between the competing design goals of security and functionality of information and process control systems.
Protection of Infrastructure
Authors: Banks David L. , Hengartner Nicholas 
Published: September 2014

Abstract
Critical infrastructure consists of the basic mechanisms and services essential for a society to function. It must be protected against natural and deliberate damage. These protective efforts should be selected according to careful risk analysis, with consideration of opportunity costs and the interactions between different kinds of investment. For natural threats, there is a large literature that often focuses on the extreme events; for deliberate threats, one must also consider game theory.
Credit Risk Models
Authors: Laurent Jean‐Paul 
Published: September 2014

Abstract
We first describe specific features of credit risk. Subsequently, we consider the computation of loss distributions for credit portfolios in an individual model, also known in the credit literature as a bottom‐up approach. This allows to deal with discrepancies between marginal default probabilities. Dependence between default dates is modelled through a factor model. We focus on the Gaussian copula, which has become a standard in the credit derivatives market. In that framework, the characteristic function of the loss can be written in closed‐form.
Enterprise Risk Management (
ERM
)
Authors: Ai Jing , Brockett Patrick L. 
Published: September 2014

Abstract
Enterprise risk management (ERM) is a recent risk‐management technique for managing a portfolio of risks in a holistic manner. ERM has evoked interest from corporate executives, regulators, and rating agencies, among others. Under the ERM framework, corporations take on risks necessary to pursue their strategic objectives, consistent with their “risk appetite”. The core of the ERM process is efficient risk integration, in which interrelations among risks and risk prioritization are highlighted. Certain risk measures, aggregation methods, or other mathematical modeling approaches are usually involved in its implementation. Effective risk reporting and communications in a well‐designed organizational structure are also essential for the success of ERM. The ultimate goal of ERM is to move beyond the initial incentive of fulfilling a compliance need to achieve real economic value.
R&DPlanning and Risk Management
Authors: A. Cox Louis 
Published: September 2014

AbstractThis article discusses quantitative concepts and methods for making risk‐management decisions about R&D projects. Such decisions include what projects to fund, the order in which to attempt different activities within an R&D project, when to abandon projects that are not working out as hoped, and how to structure the incentives of R&D project managers and investors to take best advantage of the opportunities represented by risky projects.
As Low as Reasonably Practicable/As Low as Reasonably Achievable
Authors: Bedford Tim 
Published: September 2014

Abstract

The
ALARP principle
and the closely related
ALARA
(as low as reasonably achievable)
principle
, refer to the notion that risks should be “as low as reasonably practicable”. This principle is applied, particularly in the United Kingdom, in risk regulation. Broadly speaking, activities involving high risks will not be allowed, activities with negligible risks will not be subject to restriction, while those activities in between will be subject to cost–benefit‐based risk reduction. The paper discusses the following topics: the principles of ALARP, in particular, the notion of disproportion; how risk is measured, in particular, the notions of individual and societal risk; the concept of tolerable risk arising in the UK Sizewell B Enquiry; how the cost–benefit trade‐off is made, including valuations of a statistical life; criticism of the ALARP principle; and an example.
Risk Management of Construction Defects
Authors: Steffey Duane L. 
Published: September 2014

Abstract
Risk management of construction defects involves the collection and analysis of data to ascertain the empirical basis for alleged defects and to assess the producer's or consumer's risk regarding possible actions. In the context of a dispute between two or more parties, risk managers also make decisions concerning alternative options for resolution. Defects are usually identified through quality inspections and tests during construction or sometimes through the manifestation of problems after the completion of construction. Statistical sampling and estimation are used to characterize the prevalence of defects. Important considerations include the selection of a suitable sampling design, determination of an adequate sample size, and control of potential sources of bias. Defects attributable to design, materials, and method of construction may require qualitatively different approaches compared to the sampling plans implemented for defects attributable to workmanship. Repair options often vary in scope and unit cost, with implications for the associated financial risks.
Chernobyl Nuclear Disaster
Authors: Howard John 
Published: September 2014

Abstract

On April 26, 1989, an explosion and fire at the Chernobyl nuclear power plant in Ukraine released an estimated 1.4 × 10
18
Bq of ionizing radiation into the surrounding atmosphere. Acute health effects were noted in 237 highly exposed emergency workers, 134 were treated for acute radiation syndrome, and 28 died. An estimated 600 000 people were involved in the recovery and cleanup operations after the disaster and about 116 000 nearby residents were evacuated from the area, which became known as the
Chernobyl exclusion zone
. Areas of the countries surrounding the plant—Belarus, the Russian Federation, and Ukraine—are considered “contaminated” based on cesium‐137 measurements. On the basis of these measurements, about 3% of the former Soviet Union is considered contaminated by cesium‐137 densities greater than 37 kBq m
−2
. Radioactive material spread throughout the Northern Hemisphere, but radiation doses to populations outside Ukraine, Belarus, and the Russian Federation were relatively low and showed large differences depending on the occurrence of rainfall. Thyroid cancer in children exposed at a young age to Chernobyl is widely accepted as causally associated with the Chernobyl disaster. Other than childhood thyroid cancer, no other health effects have been considered related to Chernobyl exposure, but recently data on emergency workers may indicate some increase in morbidity and mortality caused by leukemia and solid organ cancer. Large areas of Ukraine, Belarus, and the Russian Federation are still excluded from agricultural uses, and other environmental uses are subject to strict controls. In the years ahead, a safer containment structure for the residual risk of the melted nuclear fuel still inside the quickly built “sarcophagus” or “shelter object” will need to be constructed. Chernobyl continues to serve as a model of international collaboration to assess and manage risks to human health and to the environment from the world's largest nuclear disaster.
Environmental Toxicology, Statistics in
Authors: Bailer A. John , Oris James T. 
Published: September 2014

Abstract
Environmental toxicity refers to the impact of a hazard or stressor at all levels of biological organization. This impact can range from molecular and cellular levels to whole organism, population, ecosystem, or landscape levels. The statistical methods employed to examine these impacts will reflect the spectrum of the level of organization and the measurements taken at this level. The methods associated with this spectrum are described and illustrated in this article.
Latin Hypercube Sampling
Authors: Iman Ronald L. 
Published: September 2014

AbstractThis entry discusses the use of computer models for such diverse applications as safety assessments for geologic isolation of radioactive waste and for nuclear power plants; loss cost projections for hurricanes; reliability analyses for manufacturing equipment; transmission of HIV; and subsurface storm flow modelling. Such models are usually characterized by a large number of input variables (perhaps as many as a few hundred), and usually, only a handful of these inputs are important for a given response. In addition, the model response is frequently multivariate and time dependent. Latin hypercube sampling (LHS) uses a stratified sampling scheme to improve on the coverage of thek‐dimensional input space for such computer models. This means that a single sample will provide useful information when some input variable(s) dominate certain responses (or certain time intervals), while other input variables dominate other responses (or time intervals). By sampling over the entire range, each variable has the opportunity to show up as important, if it indeed is important. If an input variable is not important, then the method of sampling is of little or no concern. The values of the stratified sampling scheme can be paired to ensure a desired correlation structure among thekinput variables. LHS is more efficient than simple random sampling in a large range of conditions.
Point Source Modeling
Authors: Diggle Peter J. 
Published: September 2014

AbstractWe consider the problem of investigating spatial variation in disease risk around one or more preidentified point sources of pollution. This topic has its origins in concerns about the health effects to the surrounding population of potentially toxic emissions from industrial sites. We describe approaches to the problem that apply the theory of spatial point processes to the analysis of data from an epidemiological case–control study. We first formulate an idealized model in which cases and controls are modeled as inhomogeneous Poisson point processes whose spatially varying intensities are linked by a relative risk function ρ(x), wherexdenotes location. Typically, ρ(·) is modeled as a function of the distance, and possibly the orientation, ofxrelative to the point source of interest. We show how likelihood‐based inference about ρ(·) can be carried out by fitting a nonlinear binary regression model to the case–control labels, conditioning on the locations of cases and controls. We describe several specific parametric models that have been suggested in the literature, and a nonparametric alternative in which ρ(·) is modeled as a smooth, but otherwise arbitrary, function of distance.
Risk–Benefit Analysis for Environmental Applications
Authors: Gaylor David W. 
Published: September 2014

Abstract
The issue examined in a risk–benefit or risk–risk analysis is the trade‐off between losses suffered from illnesses or diseases, perhaps due to exposures to an environmental contaminant, and losses due to toxic effects induced by environmental remediation, e.g., chemicals used in cleanup or disinfectants employed to reduce microbial exposures. Risks and benefits may be incurred by different individuals. A management or societal issue arises as to whether the benefits (e.g., reductions of the severity or incidences of illnesses, diseases, or death) outweigh the risks introduced by interventions, e.g., disinfectant by‐products from chemicals used for drinking water treatment. A risk–benefit issue also arises for drugs with side effects and vitamins or essential minerals that become toxic at high doses. A procedure is described for estimating the optimum dose for a chemical intervention, drug, vitamin, or essential mineral that minimizes the total expected loss from the risk of an illness or disease and the loss induced by chemical, drug, or dietary intervention.
Group Decision
Authors: Morton Alec 
Published: September 2014

AbstractThe practice of risk assessment depends heavily on insights from the theory of the individual decision maker, both normative and behavioral. However, most risk related decisions impact on, and involve, multiple parties who may have interests which are not congruent. This article considers what light normative and behavioral theories of group decision can shed on risk assessment problems.This article reviews ideas from game theory, such as the Nash equilibrium and the Nash bargaining solution, and relates them to a nuclear‐waste management example. Key ideas from the theory of social choice, such as Arrow's Paradox and Sen's Impossibility of a Paretian Liberal are also discussed. Alongside this normative theory, some of the key findings from the social psychological literature about how preferences are formed in groups are also described, focusing particularly on theories of social influence and phenomena such as polarization and groupthink.The theories of group decision alert the analyst to ways in which group decisions can be badly made: they may be unfair, unsustainable, arbitrary, or based on poorly shared information, or deliberately or accidentally suppressed dissent. Fortunately, it is possible to design decision processes which make appropriate use of risk and decision analytic technologies, but which are also responsive to the social context in which the decision takes place.
Counterterrorism
Authors: Karr Alan F. 
Published: September 2014

AbstractIn today's world of complex politics and instant communication, terrorist attacks have assumed massive importance in terms of risk reality and risk perception. Strategies, mainly by national governments, to prevent, deter, mitigate, or otherwise respond to terrorist attacks are known ascounterterrorism.This article provides an overview of counterterrorism drawn exclusively from public information. The problems and issues are placed in a context of risk and risk perception, but we argue that extant paradigms for risk analysis may not apply, for at least two reasons. First, most approaches will be overwhelmed by false positives, given the extreme rarity of events that have occurred and the (conceptual to methodological) difficulty of predicting events of unknown nature. Second, not much of the data is available for broad scientific analysis and verification. Public attitudes and perceptions are discussed throughout.
Considerations in Planning for Successful Risk Communication
Authors: Jardine Cynthia G. 
Published: September 2014

AbstractSuccessful risk communication does not occur serendipitously—it is the result of careful planning and consideration of multiple needs and objectives. Some considerations for planning for successful risk communication are presented here. However, this list is neither fully comprehensive nor exhaustive. The best overall guidance is to try to view the risk through the eyes of those who may legitimately and rationally view the nature and consequences of the risk differently. Evoking an empathetic understanding of different perspectives of risk, together with the application of good judgment about the nature of the risk, is ultimately the key to initiating a productive dialogue on risks.
Stakeholder Participation in Risk Management Decision Making
Authors: Jardine Cynthia G. 
Published: September 2014

AbstractStakeholder participation in risk management processes is becoming increasingly recognized as a means of producing decisions that are responsive to varying interests and values, including those of the community. Some considerations for planning for successful stakeholder involvement are outlined. Goals against which stakeholder participation efforts can be evaluated are also provided. However, the “right” process of participation must be determined on the basis of the nature of the risk and the impact of the decision.
Dirichlet Process, Simulation of
Authors: Laud Purushottam W. , Pajewski Nicholas M. 
Published: September 2014

Abstract
We describe the Dirichlet process and its basic implementation in Bayesian hierarchical modeling. In particular, we discuss Dirichlet process mixture models with emphasis on computational strategies for both conjugate and nonconjugate forms of these models in the context of Markov chain Monte Carlo simulation.
Prior Distribution Elicitation
Authors: Quigley John , Bedford Tim , Walls Lesley 
Published: September 2014

AbstractAn overview of key issues associated with the elicitation of a prior probability distribution is provided. The nature of subjective probability is discussed, and the main causes of biased assessments are identified. Evidence is presented suggesting that people can improve their probability assessments under the correct conditions. The ways in which the elicitation of a prior distribution can be embedded within a process to minimize the impact of bias are examined. More advanced issues, namely validation of a prior distribution and reducing the cognitive burden on experts, are described.
Bootstrap and Jackknife, Overview
Authors: Martin Michael A. , Roberts Steven 
Published: September 2014

AbstractA quarter of a century ago, the advent of fast, cheap computing saw the emergence of computationally intensive methods that sought to exploit the data itself as a resource for information rather than relying on restrictive classical distributional assumptions that often failed to hold in practice. Methods such as permutation procedures and the whimsically named jackknife and bootstrap have found wide use among practitioners in a variety of contexts. In this article, the basic uses of resampling techniques are described, with particular attention to bias and variance estimation, and confidence interval construction.