bluuebunny/crossref_metadata_2025_split huggingface dataset:
Benchmark Analysis
Authors: Crump Kenny S. 
Published: September 2008

Abstract
Benchmark analysis is a statistical tool used to set guidelines for exposures to toxic substances. The benchmark dose (BMD) is a dose or exposure level to a toxin that corresponds to a predetermined increase in risk (benchmark risk or BMR) of an adverse health response. An exposure guideline is set by dividing a statistical lower bound on the BMD (BMDL) by factors that account for various limitations in the underlying database for the toxin. Previously, a no‐observed‐adverse‐effect‐level (NOAEL) was used in place of the BMDL. The BMDL offers several advantages over the NOAEL, including better accounting for the shape of the dose–response and the size of the study. Issues that need to be resolved in a BMD analysis include the choice of the BMR, the mathematical form assumed for the dose–response, and how to reconcile BMD calculated from binary and continuous endpoints.
Combining Information
Authors: Piegorsch Walter W. , Bailer A. John 
Published: September 2008

Abstract

An area of growing importance in quantitative risk analysis concerns the combination of information from diverse sources. A common rubric for combining the results of independent studies is
meta‐analysis
. The goal of the methodology is to bring together results of different studies, reanalyze the disparate results within the context of their common endpoints, increase the sensitivity of the analysis to detect the presence of adverse effects, and provide a quantitative analysis of the phenomenon of interest based on the combined data. This entry discusses some basic methods in meta‐analytic calculations, and includes commentary on how to combine or average results from multiple models applied to the same set of data.
Environmental Monitoring
Authors: McBean Edward A. , Sharma Mukesh 
Published: September 2008

Abstract
While risk assessment and management procedures are now widely employed, the need for routine and specific environmental monitoring is demonstrated as important inputs to the procedures. Approaches for defining the conceptual framework for risk assessment are provided, including the need to assemble data to assist in quantifying the chemicals in each of the environmental media (e.g., air, soil, and water) and the transformation between media. The elements of food‐chain modeling are described, to demonstrate how the migration pathways may develop between media and elements of the food chain. Specific examples of migration and transformations of chemicals in the environment are explored, with reference to statistical procedures to assist in the data characterization.
Extreme Event Risk
Authors: Brillinger David R. 
Published: September 2008

Abstract
“Extreme event” is a vague concept with a variety of formal definitions based on specific cases and contexts. Particular extreme events include, great earthquakes, floods, droughts, nuclear meltdowns, space shuttle losses, deaths, giant sea waves, stock crashes, landslides, storms, hurricanes, tornadoes, wildfires, and consequences of any of the preceding. The event of interest might be caused by man or by nature. They may occur in time or space. The size might be measured in deaths or costs. The study of extreme event situations has led to important research results and management methods. The specific concern of the entry is quantitative formulation of the problem.
Fate and Transport Models
Authors: Pistocchi Alberto 
Published: September 2008

Abstract
In the following section we provide general background information about mathematical descriptions of phase partitioning of chemicals, mechanisms of their transfer between and within environmental media, and degradation, also introducing the most common mathematical representations of the processes described. Eventually, such representations are combined in a general equation used for fate and transport modeling: the advection–dispersion–reaction equation (ADRE). The processes described by the ADRE are very general; depending on the specific medium (soil, water, air) where they take place, the different terms of the equation are represented differently.
In a subsequent section, we describe specific cases of the ADRE in relation to surface water, soil/groundwater, and air, which are the domains where it is normally solved. For each case, we introduce some examples of publicly available computing codes that solve the equation.
In the last section, we introduce multimedia/multispecies fate and transport of chemicals using linear box models.
Gene–Environment Interaction
Authors: Umbach David M. 
Published: September 2008

Abstract
Current biomedical research is guided by the idea that genetic makeup and environmental exposures, broadly construed, work together through time to cause human disease. This idea is the broad essence of gene–environment interaction. The detailed assessment of gene–environment interaction using epidemiologic data involves issues such as how one defines such interactions in a statistical sense and how one might infer that interactions seen in observational studies reflect causal relationships. That gene–environment interaction is hard to detect and estimate, requiring much larger sample sizes than main effects, has prompted research into novel approaches to increase power and precision through both methods of data analysis methods and study designs. Efforts to make studies robust against hidden genetic population structure have prompted other innovations in study design. The needs of biomedical science continue to stimulate research toward improved methods for studying gene–environment interaction.
Global Warming
Authors: Tebaldi Claudia , Mastrandrea Michael D. , Smith Richard L. 
Published: September 2008

AbstractGlobal warming is a worldwide environmental, social, and economic challenge, with far‐reaching implications for human welfare and the health of natural systems. While this qualitative assessment is generally agreed upon by a large share of scientific, policy‐making, and stakeholder communities, a quantification of the risks associated with global warming is far from straightforward. There exist considerable uncertainties, including limits to predicting future changes in the drivers of greenhouse gas emissions (population, economic growth, etc.), incomplete scientific understanding of the response of the climate system to those emissions, and components of intrinsic randomness or unpredictability in the processes involved. From the size of the changes to be expected to their timing and from the significance of impacts to the costs of adaptation and mitigation strategies, every link of the chain from emissions, to impacts, to the effects of policy is affected by uncertainties. This article characterizes the main sources of uncertainty in modeling of the climate system and in the development of scenarios for future emissions, as well as the most rigorous efforts currently documented in the peer reviewed literature to quantify uncertainty and incorporate it into risk analysis.
Hazardous Waste Site(s)
Authors: Carlon Claudio 
Published: September 2008

AbstractHazardous waste sites are portions of land contaminated by voluntary or involuntary introduction of hazardous substances. In their several millions worldwide, they pose a relevant risk to human health and the environment. Hazardous substances can spread in the environment through volatilization, particle suspension, and water leaching. Relevant exposure to these substances can occur to residents, workers, and trespassers as well as to plants, soil organisms, birds, mammals, and fishes. Risk assessment methodologies are employed to rank sites at regional scale, to develop generic soil quality standards and to assess the risk, and tailor remediation interventions at specific site locations. It is usually a retrospective risk assessment, since the exposure has been occurring at the time of the study. The fundamentals and front‐edge developments of these methodologies are introduced and related to risk‐management practices.
Latin Hypercube Sampling
Authors: Iman Ronald L. 
Published: September 2008

AbstractThis chapter 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.
Low‐Dose Extrapolation
Authors: Piegorsch Walter W. 
Published: September 2008

AbstractIssues of extrapolation in risk analytic studies are reviewed for cases where the design of a study requires the data to be taken at fairly high‐dose levels, but where decisions on occupational or environmental exposure standards to a toxic agent must be extrapol ated from the high‐dose exposure data to low‐dose levels of the agent.
Modifiable Areal Unit Problem (
MAUP
)
Authors: Sexton Ken 
Published: September 2008

Abstract
Deciding on an appropriate research design, interpreting study results, or choosing a suitable public policy to mitigate environmental risk implies a decision, either implicit or explicit, about spatial scale and resolution. The modifiable areal unit problem (MAUP) is a formal statement of the dilemma facing researchers, risk assessors, and policy makers when they use geographical data—results of statistical analyses usually depend on subjective decisions about geographical units of analysis and the level of data aggregation within those units. Consideration of the MAUP is essential for the development of effective and efficient strategies for managing environmental health risks.
Potency Estimation
Authors: Bailer A. John , Piegorsch Walter W. 
Published: September 2008

AbstractA common goal in environmental risk assessment is description of the hazardous agent's toxic or otherwise‐harmful effects, often for comparison with others agents of related structure or exposure. Summary measures are most useful in this regard, as these describe the potency of an agent or some product of an agent on the organism or system under study. A number of quantitative summaries can be used to measure or define potency, each based on a slightly different feature of the observed dose–response. We consider a few of these in this article.
Remote Sensing
Authors: Myers Wayne , Patil Ganapathi P. 
Published: September 2008

Abstract
Risk assessment requires information on exposure, sensitivity, condition, and resiliency of entities under investigation. In environmental contexts, this typically requires geographically specific information that can often be obtained most quickly and at least cost through remote sensing in conjunction with geographic information systems (GISs). We introduce and illustrate major aspects of remote sensing using forest fires as an example context. Signal selection is considered for the electromagnetic spectrum, along with formulation of pseudosignals as environmental indicators. Conventional classification sets the stage for modeling of spatial interaction among signals as polypatterns. Detection of landscape changes is considered in both conventional and image modeling modes. Need for partnership between remote sensing analyst and risk analyst is underscored.
Soil Contamination Risk
Authors: Carlon Claudio 
Published: September 2008

Abstract
Soil is a “vital” medium hosting most of the biosphere and is essentially a non‐renewable natural resource owing to the very slow processes under its generation. It has fundamental functions in the water and biogeochemical cycles. It is also the platform for human activities and landscape. The old idea that soil could degrade any sort and amount of contamination without being affected in its functioning has been found incorrect, and it is now recognized that soil protection is highly relevant for conservation of the terrestrial ecosystem, for the safeguard of human health, and for agriculture. Risk assessment methodologies have been developed for assessing the risk posed by soil contaminants to the terrestrial ecosystem and to human health. They have to deal with a large number of uncertainties owing to poor knowledge and limited modeling capability. An overview of sources of uncertainty is provided for both human health and ecological risk assessment. This is followed by a description of how soil quality standards are the product of sound science and conventional assumptions depending on risk societal valuing.
Spatiotemporal Risk Analysis
Authors: Lawson Andrew B. 
Published: September 2008

Abstract
This article focuses on the issues related to risk assessment when the domain consists of space and time. Spatiotemporal risk assessment could involve separate analyses of risk in a geographical domain, risk in a temporal window, or the interaction of space and time. Often the focus will be on magnitude and frequency of an outcome but this will have a spatial and temporal expression. The link with surveillance for adverse outcomes is made, and health risk is a focus of the review, although the concepts illustrated can be applied in other areas such as financial or industrial risk analysis.
Vulnerability Analysis for Environmental Hazards
Authors: Cutter Susan L. 
Published: September 2008

Abstract
This entry details a spatial social science perspective for environmental hazards vulnerability analysis. Vulnerability science is an interdisciplinary approach that integrates the characteristics of potential hazards such as magnitude, frequency, and duration (physical vulnerability) with characteristics of people and the built environment that either increase or decrease their impacts (social vulnerability). It is the combination of the physical and social processes and associated outcomes that contribute to the overall vulnerability of places. Spatial social science methods and tools (such as geographic information systems) provide the integrative mechanism for comparing vulnerability at various geographic scales such as census tracts, counties, or cities.
Credit Risk Models
Authors: Laurent Jean‐Paul 
Published: September 2008

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.
Insurance Applications of Life Tables
Authors: Pitacco Ermanno 
Published: September 2008

Abstract
Mortality constitutes one of the main causes of risk in a life insurance portfolio (or pension plan). Many risk factors contribute to determine the mortality in a portfolio. In particular, at an individual level, the insured's age has an important role. In this article, we first describe the features of a (period) life table which constitutes the basic tool to represent the age pattern of mortality, and the applications of life tables to the calculation of expected present values (or “actuarial values”) in life insurance. Next, we introduce the ideas underlying the projected life tables, meant as a tool to represent future mortality. Finally, we focus on randomness in the results of a life insurance portfolio (cash flows, profits, etc.), and the use of stochastic models.
Risk Classification/Life
Authors: Haberman Steven , Olivieri Annamaria 
Published: September 2008

AbstractThis article gives an overview of the main models used to represent heterogeneity in life insurance. We focus in particular on two classes of models: individual models, which are suitable to represent heterogeneity aspects that are observable at the issue of an insurance; and collective models, which are suitable to assess heterogeneity due to unobservable risk factors.The adoption of (at least some) individual models is common practice in life insurance, in particular for pricing or, more generally, for risk management. On the other hand, collective models are used for certain mortality investigations, in particular for suggesting an appropriate description of the age‐specific mortality shape in some insured portfolios.
Solvency
Authors: Sandström Arne 
Published: September 2008

Abstract
Solvency is the ability of an insurance company to pay future claims. The required solvency capital is compared to available capital. The supervisor can impose the insurance company to act depending on the level of the available capital in comparison to the required capital. In a risk‐based system the required capital should be calculated including all quantitative risks and using a total balance sheet approach, i.e., recognizing both the asset and liability sides of the balance sheet, as well as the interdependence between them. A pragmatic solution to nonnormality and nonlinearity is discussed.
Simulation in Risk Management
Authors: Mcleish Don L. , Metzler Adam 
Published: September 2008

Abstract
Risk refers to the possibility and the fear of things going wrong (i.e., some combination of events that have negative impact) and the magnitude of the losses resulting from these events. The concept of risk varies depending on the perception of different individuals and in some cases the “perceived” or “risk‐neutral” probabilities of events are more important than the real‐world probabilities, because they drive publicly traded asset prices in the immediate term. The Basel committee provides a framework for regulating minimum capital requirements for banks to cover losses incurred under five different types of risk: credit risk, market risk, operational risk, liquidity risk, and legal risk, and many of these categories carry over to different types of industry. Complex structures or organizations are exposed to many different risk factors or types of risk. The probability of one or more risk events is often very small and difficult to assess for lack of historical experience. There is a relationship among risk factors that may increase the probability of them occurring in combination. Because of the complexity, simulation methodology is quickly becoming the method of choice for evaluating and providing safeguards against the potential losses resulting from risk exposure. In this article, we discuss the use of Monte Carlo simulation as a cost‐effective method to quantify the financial risks of a corporation.
Structured Products and Hybrid Securities
Authors: Carverhill Andrew 
Published: September 2008

AbstractA “structured product” is a derivative financial security, whose cash flows are “structured” to depend on more basic financial values, so as to suit the parties to the arrangement. An example is a Guaranteed Equity Bond (GEB), which might be issued with a lifetime of 5 years, at the end of which it pays the principal invested, plus say 90% of any gain, if the principal had been invested in the stock index over the 5 years. In this survey, we describe some of the main categories of structured products, and describe the motivation for their design, and then we briefly discuss the valuation and risk management of these products.
Value at Risk (
Va
R
) and Risk Measures
Authors: Schmitt Christian , Zagst Rudi 
Published: September 2008

Abstract
In this article, we present different ways of measuring and quantifying risk. We discuss the theoretical background of several risk measures and illustrate their potentials and limitations. Giving a historical overview, we start with the variance and lower partial moments and present the concepts of shortfall probability and expected shortfall. The main focus of this article is the value at risk, one of the most important risk measures in the financial services industry. We analyze different calculation methods, which use historical data or simulations. Finally, we discuss the concept of coherence that is a set of requirements, which a suitable risk measure should meet.
Volatility Modeling
Authors: Andersen Torben G. 
Published: September 2008

AbstractThis chapter discusses the main approaches to return volatility modeling based on historical time series observations on the underlying price. It starts from simple filtering estimates including historical volatility measures and exponential smoothers used by Risk Metrics, then progresses through ARCH and more general stochastic volatility modeling procedures and ends with the recent development of realized volatility measurement, modeling and forecasting based on the increasing availability of high‐frequency intraday transaction and quote data from financial markets.
Credit Value at Risk
Authors: Wong Samuel Po‐Shing 
Published: September 2008

AbstractFinancial crises cause losses in many portfolios. The measures of value at risk quantify the severity of the consequential negative impacts and are therefore used as the capital buffers prepared for financial risks. Basel II Accord classifies financial risks into the following three categories:Market risk: the loss caused by price fluctuations.Credit risk: the loss triggered by default events.Operational risk: the loss resulted from factors other than those in 1 and 2.The credit value at risk plays a pivotal role in the reserve capital assignment because credit risk is the most contributing component among the three factors listed. In this survey, we describe the definition and computation of the credit value at risk and give some guidelines for stress testing. Some implementation details of credit value at risk are also presented.
Default Correlation
Authors: Kapadia Nikunj 
Published: September 2008

AbstractDefaults across firms in the economy are correlated partly because firms are exposed to common risk factors, and partly because of frailty or contagion risk. In modeling joint default risk, several approaches have been introduced. In structural models of default risk, joint default risk may be introduced by allowing assets values to be correlated. In reduced form intensity based models, default intensities can be allowed to be correlated. However, conditional on the default intensity, each default event is independent. Finally, the correlation between defaults may be directly modeled via a copula correlation. We describe and discuss these approaches to modeling default correlation.
Bayesian Analysis andMarkovChainMonteCarlo Simulation
Authors: Medova Elena 
Published: September 2008

AbstractThis chapter describes the use of hierarchical Bayesian analysis using Markov chain Monte Carlo (MCMC) estimation to obtain the parameters of a peaks‐over‐threshold (POT) extreme value theory model of extreme operational losses. Principal MCMC techniques are described and a simple example which illustrates the concepts, including economic capital allocation, is discussed.