bluuebunny/crossref_metadata_2025 huggingface dataset:
Asset–Liability Management for Life Insurers
Authors: Sherris Michael
Published: September 2008

This article outlines approaches used to quantify risk using ALM for life insurers.
Asset–Liability Management for Nonlife Insurers
Authors: Brehm Paul , Venter Gary
Published: September 2008

Asset–liability management for nonlife insurers is more complicated than quotidian duration‐matching analysis of changing interest rates. When stochastic liabilities are correlated with a number of financial variables, enterprise‐wide models are needed to quantify the impacts of possible investment and hedging strategies.
Credit Risk Models
Authors: Laurent Jean‐Paul
Published: September 2008

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.
Individual Risk Models
Authors: Vernic Raluca
Published: September 2008

For an insurance company, evaluating the aggregate claims distribution is generally not an easy task. Among various methods used for such computations, recursive methods have the advantage of giving accurate values with considerable savings in the computing time. The purpose of this article is to give an overview of such exact and approximative recursions for the individual risk model.
Insurance Applications of Life Tables
Authors: Pitacco Ermanno
Published: September 2008

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

This 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

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.
Numerical Schemes for Stochastic Differential Equation Models
Authors: Higham Desmond J. , Kloeden Peter E.
Published: September 2008

A very brief overview of stochastic differential equations and their numerical simulation is given, with emphasis on the context of financial modeling. References to the recent literature are provided.
Simulation in Risk Management
Authors: Mcleish Don L. , Metzler Adam
Published: September 2008

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

A “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

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

This 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

Financial 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

Defaults 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.
Default Risk
Authors: Dowd Kevin
Published: September 2008

This article examines default risk and discusses a number of ways in which it can be assessed. These include the use of credit ratings provided by ratings agencies; contingent claims approaches, including the Merton and Kealhofer, McQuown, and Vasicek (KMV) models; ratings migration approaches, including the CreditMetrics model; and intensity approaches.
Bayesian Analysis andMarkovChainMonteCarlo Simulation
Authors: Medova Elena
Published: September 2008

This 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.
Extreme Value Theory in Finance
Authors: Brodin Erik , Klüppelberg Claudia
Published: September 2008

Extreme value theory is a practical and useful tool for modeling and quantifying risk. In this article, after introducing basic concepts, we indicate how to apply it within a financial framework.
Operational Risk Modeling
Authors: Cruz Marcelo
Published: September 2008

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.
Arsenic
Authors: Slayton Tracey M. , Lewis Ari S. , Beck Barbara D.
Published: September 2008

Arsenic is a naturally occurring element, found in soil, some groundwaters, and food. Throughout the world, including Taiwan, India, Mongolia, and Bangladesh, human populations are exposed to high levels of arsenic in drinking water. Short‐term exposures to large doses of arsenic can cause health effects such as encephalopathy, peripheral neuropathy, and death. Long‐term human exposure to high levels of arsenic causes a variety of noncancer health effects (including pigmentation changes, hyperkeratosis, and vascular effects) and also cancer (including skin, bladder, and lung cancer). However, consistent health effects have been observed only in populations outside the United States exposed to relatively high concentrations of arsenic in drinking water, often in populations suffering from nutritional deficiencies that may increase susceptibility. To standardize quantitative risk assessments for arsenic, US regulatory agencies have developed arsenic‐specific toxicity factors (reference doses for noncancer effects, and cancer slope factors for cancer), based primarily on Taiwanese populations exposed to high levels of arsenic in drinking water. These analyses contain conservative elements; what is particularly important is that there is convincing human and mechanistic data to support a nonlinear dose–response relationship for arsenic. Overall, the arsenic cancer slope factor is likely to overestimate cancer risk at low levels of exposure.
Health Hazards Posed by Dioxin
Authors: Paustenbach Dennis J.
Published: September 2008

This 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.
Design of Reliability Tests
Authors: Kececioglu Dimitri B. , Chen Xiaofang
Published: September 2008

Reliability is the probability that a product can perform its required functions for a specified function period without failure under certain environments and stress levels. Reliability information is very important to manufacturers. It enables them to produce better products for their costumers. Reliability tests are necessary in designing and manufacturing a product to guarantee its reliability and quality. This article introduces the basic ideas of how to design reliability tests, their objectives, test types, and test procedures.
Reliability Growth Testing
Authors: Pulcini Gianpaolo
Published: September 2008

Reliability growth is the process by which the reliability problems of a product are identified and corrected by introducing modifications in the product design and/or manufacturing process. Accordingly, the reliability of the product grows as the product goes through successive stages of the development program. Although reliability growth generally refers to improvements in a product's reliability, a very important field where reliability growth programs can find large application is the control of industrial accidents.

We discuss a number of stochastic models that are able to describe the reliability growth, either when improvement actions are introduced at failure occurrence (test‐analyze‐and‐fix program), or when the corrective actions are delayed and implemented in block at the end of each stage. Such models are classified intocontinuousanddiscontinuousmodels according to the assumed behavior of the product reliability with test time. Special attention is devoted to the nonhomogeneous Poisson process with power‐law intensity function, namely, the power‐law process, which is the stochastic representation of the well known deterministic growth model of Duane.
Reliability of Consumer Goods with “Fast Turn Around”
Authors: Den Ouden Elke , Lu Yuan , Brombacher Aarnout
Published: September 2008

Development of consumer goods that meet the buying and using expectations of the consumers is no longer a straightforward job. Technology becomes available at increasing pace, while at the same time the product life cycles ever shortening. Companies are challenged to bring innovative products faster to the market, with inherently higher uncertainties on the consumer expectations of these products. In this context, reliability has a different meaning and the design methods and tools required to manage reliability, as well as test strategies in this context, are different from the classical ones.
Non‐ and Semiparametric Models and Inference for Reliability Systems
Authors: Peña Edsel A. , Taylor Laura L.
Published: September 2008

Precise and reliable knowledge of the performance of deployed systems in the engineering, biomedical, public health, military, economic, and business areas enables an informed assessment of the risk of failure and/or the quality of systems. In the study of the risk and the quality of such systems, a conceptual model is that of reliability systems. This article discusses non and semiparametric modeling and inference of reliability systems. In the first section, a review of some basic notions of reliability systems is presented. The next two sections provide nonparametric models when there are no covariates impacting the performance of the system, and the methods of estimating the survivor function for the system life based on possibly right‐censored data. An illustrative example is given for a randomly generated dataset. The next section then discusses semiparametric models, which incorporates covariates, with primary focus on the Cox's proportional hazards model. Inference procedures for these semiparametric models are discussed. Finally, mention is made of dynamic models which are those that incorporate the changes occurring dynamically in a system, as time progresses.
Degradation and Shock Models
Authors: Finkelstein Maxim
Published: September 2008

The standard assumption in shock models are that the failure of a system is caused either by the cumulative effect of a large number of shocks or by a shock that exceeds a certain critical level. In this paper, we discuss both possibilities and consider some generalizations of existing models. Two generic point processes of shocks are considered: the renewal and the Poisson processes. When the average number of renewals to reach the degradation threshold of accumulated damage is large, the asymptotic renewal theory is used for obtaining the first passage probabilities. For the specific case of the homogeneous Poisson process of shocks, some new models with different criterions of failure are considered. For instance, we consider the setting when the failure occurs only when the successive shocks are “closer” than the predetermined value (deterministic or random).
Recurrent Event Data
Authors: Stocker Russell S.
Published: September 2008

Many lifetime data studies involve units that experience multiple‐ordered events of the same nature. The resulting data is commonly termed
recurrent event data
. This article presents some of the important aspects that need to be considered when modeling this type of data. It surveys several of the models that are found in the literature. The models are applied to a real data set for comparative purposes.
Competing Risks in Reliability
Authors: Bedford Tim , Alkali Babakalli , Burnham Richard
Published: September 2008

Competing risks models are used to analyze operational field data in reliability. They assume that different failure risks and maintenance events compete with one another to be the cause of the system being taken out of service. These risks censor each other and it is because of this censoring that problems arise when trying to identify marginal distributions for the time to failure.

These problems can only be surmounted by making assumptions about the underlying dependencies between the competing risks variables, requiring a deeper understanding of the maintenance policies in place.
Imprecise Reliability
Authors: Coolen Frank P.A. , Utkin Lev V.
Published: September 2008

We present a concise overview of imprecise reliability, particularly focusing on reliability theory with uncertainty quantifiedvialower and upper probabilities. We discuss the main approaches and opportunities of the theory, include references to guide further study, and briefly discuss some research challenges.
Lifetime Models and Risk Assessment
Authors: Lee Mei‐Ling Ting
Published: September 2008

In this article we review several methods used in analyzing lifetime data. Lifetime data have found many applications in biomedical research, engineering, as well as in social sciences. Lifetime data can be defined as the times to the occurrence of a certain event. Statistical methods for analyzing lifetime data include the definition of survival time, censored data, prediction of probability of survival, prediction of instantaneous hazards, and evaluation of cure rates. We review different types of lifetime data, methods for estimating hazard and survival functions, methods for risk assessment, as well as multivariate methods for survival data. Lifetime models based on stochastic processes hitting a boundary, such as threshold regression for risk assessment, are also discussed.
Markov Modeling in Reliability
Authors: Trivedi Kishor S. , Selvamuthu Dharmaraja
Published: September 2008

We present an overview of evaluating the reliability of the complex systems with Markov modeling. In this article, the definition of reliability and the description of basic reliability models are given. Markov models are discussed in detail and illustrations are provided to explain the technique of Markov modeling.
Condition Monitoring
Authors: Banjevic Dragan , Jardine Andrew K.S.
Published: September 2008

Condition monitoring (CM) is a set of various techniques and procedures used in industry to measure the “parameters” of the state/health of equipment, or to observe conditions under which the equipment is operating. People apply CM for early detection of signs of malfunctioning and faults, and then for fault diagnosis and timely corrective or predictive maintenance. The whole combination of CM data acquisition, processing, interpretation, fault detection, and maintenance strategy is called the
CM system
/
program
(alternatively, condition‐based maintenance (CBM)). The most common CM techniques are vibration analysis, tribology (oil/debris analysis), visual inspections, current monitoring, conductivity testing, performance (process parameters) monitoring, thermal monitoring, corrosion monitoring, and acoustic (sound/noise) monitoring. The three major steps in a CM system are data acquisition, data processing, and data assessment for decisions (maintenance decision making and fault diagnostics and prediction). This article describes the key points of all three major steps, including CBM; gives a short history of CM; discusses the implementation, advantages and disadvantages of CM; comments on the future development of CM; and recommends further reading. An example of CM implementation is also included.
Maintenance Modeling and Management
Authors: Scarf Philip A.
Published: September 2008

The specification of a maintenance design is at the heart of maintenance modeling and management. This design is essentially a structured description of the system of interest in terms of its parts (components, sockets, and their interrelationships), and prescribed maintenance rules for the components in the system. The maintenance rules are determined by the following: the risk that component failure poses to system performance, the economic priorities of the system owner and the maintainer, the availability of data relating to components, and the interrelationships beteen components within the system. This article describes how one might determine a structured description of a system of interest, how maintenance actions for the resultant components might be chosen, and then how these maintenance actions may be optimized using maintenance models to provide the system owner with a set of efficient and effective maintenance rules for the system.
As Low as Reasonably Practicable/As Low as Reasonably Achievable
Authors: Bedford Tim
Published: September 2008

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.
Bayesian Statistics in Quantitative Risk Assessment
Authors: Congdon Peter
Published: September 2008

This chapter reviews some of the principles of Bayesian inference, with a focus on applications in risk assessment in various fields. The review is set in a context in which major gains in applying and estimating Bayesian models are due to improved iterative sampling techniques for estimation. Principles underlying specification of priors on parameters and also the main elements of posterior summarization are discussed. Issues of identification in more complex random‐effects models as well as ways of assessing convergence of iterative sampling by Markov Chain Monte Carlo (MCMC) techniques are mentioned in this chapter. Two worked examples including WINBUGS code illustrate some of the principles discussed; these involve ozone exceedances in the first case study, and cancer risk in relation to arsenic in drinking water in the second case study.
Behavioral Decision Studies
Authors: Brandstätter Eduard , Riedl René
Published: September 2008

Probability judgments are pivotal in guiding human decision making. The prevailing assumption in psychology is that human probability judgments are often biased. We challenge this view and present evidence of humans' remarkable capacities in judging probabilities. In particular, we discuss regression to the mean, probability judgments of conjunctive and disjunctive events, the consideration of base‐rate information, probability judgments based on small samples, judgments of personal risks, and external influences on probability judgments. We conclude that in real‐life contexts, in which the correct normative solutions are usually unknown, heuristics are useful tools for making fast and frugal judgments and decisions.
Decision Analysis
Authors: French Simon
Published: September 2008

Decision analysis is a methodology for supporting decision makers faced with complex problems usually involving conditions of uncertainty. Underpinning the analysis is the subjective expected utility model, but the methodology involves far more subtlety than simply producing an expected utility ranking. The decision makers' judgments have to be elicited in a manner that recognizes their susceptibility to behavioral biases and the precision to which they can encode their uncertainties and preferences. The conclusions of the analysis need to be explored through sensitivity analysis so that the decision makers can be assured that the indicated choice is not a spurious result of their imprecise judgments. The process needs to recognize that groups of decision makers seldom agree on all details, so there is a need to develop and confirm a consensus. Above all, there is a need to use the analysis to build a shared understanding among the decision makers rather than dictate a decision.
Expert Judgment
Authors: Hora Stephen C.
Published: September 2008

Experts are often used to provide uncertainty distributions in risk analyses. They play an important role when insufficient data exist for quantification, or when the available data or models are conflicting. Multiple steps are required in constructing a successful expert judgment process. These steps include selecting and framing the issues, identifying the experts, deciding upon an organization structure, and possibly combining the distributions from multiple experts.

Expert judgments are normally given as probabilities or probability distributions that express the uncertainty about future events or unmeasured quantities. The goodness of probabilistic judgments is measured through calibration and information, which, in turn, can be measured through a scoring rule.

Various behavioral and mathematical methods have been proposed for combining the judgments of experts. There is, however, no single method that has emerged as the best.
Group Decision
Authors: Morton Alec
Published: September 2008

The 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.
Influence Diagrams
Authors: Smith Jim Q. , Thwaites Peter
Published: September 2008

Influence diagrams have now been widely developed as one of the most useful frameworks around which a Bayesian decision analysis can be performed. These diagrams represent relationships between the variables in a decision problem. They are embellished with further quantitative information about uncertainty and utility, enabling optimal decision rules to be efficiently identified in risky situations. They apply even when there is a mixture of discrete and continuous variables in the problem description. Here, we focus on the use of the influence diagram as an elicitation tool and a framework for model analysis. Our running example concerns the discovery of optimal acts associated with a risk analysis of the safety of a cosmetic product.
Interpretations of Probability
Authors: Lenk Peter
Published: September 2008

Probability plays a central role in quantifying risk and uncertainty. In contrast to the laws of probability, which are well established, there are several different interpretations of probability. This article traces the major positions of the frequency, subjective, and logical schools, and summarizes the implications of these approaches for risk professionals. The article concludes that despite profoundly different interpretations and consequences, current industry demands require professionals to be facile in using different approaches to probability.
Multiattribute Modeling
Authors: Stewart Theodor J.
Published: September 2008

We discuss the role of multiattribute modeling, or problem structuring, in the context of multicriteria decision making. Within this context, criteria for evaluating or comparing alternative decision options are represented in terms of operationally defined attributes describing the alternatives, on the basis of which specific objectives or goals may be defined. Attention is given to the process of eliciting the criteria relevant to a particular decision context by interaction with decision makers and/or other stakeholders, after which we turn to the practicalities of constructing an operationally meaningful set of attributes. A number of guidelines are presented for purposes of selecting a final set of attributes to be used in decision analysis, often structured into a hierarchical value tree. Finally, we discuss the manner in which conflicting views of different stakeholder groups need to be taken into consideration.
Multiattribute Value Functions
Authors: Stewart Theodor J.
Published: September 2008

The multiattribute value function is a means of systematically analyzing preferences in the context of multiattribute or multicriteria decision making. We discuss in some detail the axiomatic foundations of the additive form of the multiattribute value function as an ordinal measure of preference, and the implications of these axioms for eliciting the parameters of the function. Extensions of the basic model to cardinal preference measures (generally implying nonadditive forms of the value function), and to application in mathematical programming problems (multiobjective optimization) are then discussed. We conclude with a brief discussion on the treatment of risk and of time values within the context of multiattribute value functions.
Public Participation
Authors: Bayley Clare L.
Published: September 2008

Increasingly, governments and public bodies are involving stakeholders and the general public to a far greater extent in decision‐making processes, particularly relating to the environment in which they live. A strong motivation for the uptake of participatory processes is a need for transparency and accountability in decision making. Additionally, agreements such as the Aarhus convention granting the public rights regarding access to information, public participation, and access to justice in governmental decision making on matters concerning the environment have led to an increase in stakeholder participation. Many participatory tools and strategies have been developed to involve the public and stakeholders in societal decisions, including workshops, citizen juries, focus groups, electronic forums, web polling, and other means of consultation. In this article, the objectives of public participation are discussed and the merits of a number of participatory processes at meeting these objectives are evaluated.
Uncertainty Analysis and Dependence Modeling
Authors: Cooke Roger M. , Kurowicka Dorota
Published: September 2008

Uncertainty analysis involves quantifying (joint) uncertainty on the inputs of models, propagating this through the mathematical model, and interpreting the results. We sketch the emergence of uncertainty analysis, give recent examples, and briefly illustrate current active issues. Depencence modeling and probability inversion are high on the research agenda.
Chernobyl Nuclear Disaster
Authors: Howard John
Published: September 2008

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.
Hormesis
Authors: Calabrese Edward J.
Published: September 2008

This article summarizes the historical foundations of the dose‐response relationship with particular emphasis on why the threshold dose‐response model became widely accepted within the pharmacology and toxicology communities and the hormetic dose‐response model became largely marginalized. The article describes the current resurgence of interest in the hormetic dose‐response model and why it may improve predictions in the low‐dose zone. The article provides a summary of the quantitative features of the hormetic dose‐response, its generalizability, mechanistic foundations, and its capacity for application in the risk assessment process.
Odds and Odds Ratio
Authors: Landau Sabine
Published: September 2008

This article introduces odds and motivates their use. It then proceeds to define the odds ratio.
Meta‐Analysis in Clinical Risk Assessment
Authors: Goldberg Judith D. , Watson Heather N. , Friedman Herman P.
Published: September 2008

Meta‐analysis is a statistical approach that provides a logical structure and a quantitative methodology for the review, evaluation, and synthesis of information from independent studies. Combining the results of multiple independent studies in a well‐conducted meta‐analysis provides an objective summary assessment of the overall results of a series of independent studies. With the use of meta‐analytic techniques, a series of clinical trials for example, can be summarized, and the overall treatment effect can be estimated with increased precision so that the risks of reaching incorrect conclusions from individual trials are reduced. In this article, we introduce the process of meta‐analysis, issues in the conduct of a meta‐analysis, and some statistical models that are commonly used to conduct a meta‐analysis.
Case–Control Studies
Authors: Rothman Kenneth J. , Greenland Sander , Lash Timothy L.
Published: September 2008

In a cohort study, the numerator and denominator of each disease frequency (incidence proportion, incidence rate, or incidence odds) are measured, which requires enumerating the entire population and keeping it under surveillance. A case‐control study observes the population more efficiently by using a sample of the population, which becomes the control series, in place of complete assessment of the denominators of the disease frequencies. This extra sampling step can make a case‐control study much more efficient than a cohort study of the same population, but it introduces a number of subtleties and avenues for bias that are absent in typical cohort studies. For diseases that are sufficiently rare, cohort studies become impractical, and case‐control studies become the only useful alternative.

The cases in a case‐control study should be the same people who would be considered cases in a cohort study of the same population. The definition of the source population for these cases determines the population from which controls should be sampled. Ideally, control selection would involve direct sampling from the source population, with equal probability of selection, although in many circumstances, variations in and substitutes for this ideal sampling design may be considered.
Epidemiology as Legal Evidence
Authors: Gastwirth Joseph L.
Published: September 2008

In product liability and toxic tort cases, courts need to determine whether a person's exposure to a chemical or drug caused their subsequent disease. Epidemiologic studies identify agents associated with an increased risk of disease in the exposed population and the estimated relative risk quantifies the magnitude of the risk. Epidemiology can resolve issues of general causation—does an agent cause disease—which is useful to courts in deciding whether the agent caused the particular plaintiff's disease. The factors courts use in evaluating epidemiologic evidence and how it is integrated with other available scientific evidence in the legal setting is described.
Copulas and Other Measures of Dependency
Authors: Melnick Edward L. , Tenenbein Aaron
Published: September 2008

Copulas have been developed for modeling the probability distribution of a multivariate random variable. The technique includes generating distributions with asymmetrical tail dependence structure, which is important in extreme value theory.
Efficacy
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana
Published: September 2008

This article summarizes the concepts involved in the assessment of efficacy in randomized clinical trials. In particular, various types of outcomes and their evaluation as measures of the efficacy of interventions are described.
Intent‐to‐Treat Principle
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana
Published: September 2008

This article provides an overview of the concepts involved in intent‐to‐treat analyses of randomized controlled clinical trials. Implications for the interpretation of the results of randomized trials that arise when noncompliance, missing data, and losses to follow‐up occur are described. Alternative approaches to analysis are described.
Censoring
Authors: Rai Shesh N. , Sun Jianguo
Published: September 2008

In collection and analysis of real‐life data, we always encounter incomplete data. Censoring is a type of incomplete data that arise because of a type of sampling scheme. There are many types of censorings: right censoring, left censoring, interval censoring, and Double censoring. We describe these censorings and their role in the analysis of real‐life data.
Confounding
Authors: Hernán Miguel A.
Published: September 2008

Confounding is a bias due to the presence of common causes of the exposure and the outcome. Subject‐matter knowledge can be used to draw causal directed acyclic graphs, and graphical rules like the backdoor path criterion can then be used to identify confounding and confounders, i.e., variables that can be used to eliminate confounding. Although confounding can also be defined as lack of exchangeability of the exposed and the unexposed, this nonstructural definition is less helpful for causal inference from observational data. Confounding can be adjusted for using different methods but all of them require
a priori
causal assumptions; statistical associations detected in the data are not enough to characterize confounding.
Correlated Risk
Authors: Kunreuther Howard
Published: September 2008

Natural disasters such as earthquakes, floods, and hurricanes produce highly correlated losses: many homes in the affected area are damaged and destroyed by a single event. If a risk‐averse insurer faces highly correlated losses from one event, it may want to set a high enough premium not only to cover its expected losses but also to protect itself against the possibility of experiencing catastrophic losses.
Insurability Conditions
Authors: Kunreuther Howard
Published: September 2008

Two conditions must be met before insurance providers are willing to offer coverage against an uncertain event. Condition 1 is the ability to estimate the chances of the event occurring, and the extent of losses likely to be incurred when providing different levels of coverage. Condition 2 is the ability to set premiums for each potential customer or class of customers. If Conditions 1 and 2 are both satisfied, a risk is considered to be insurable. But it still may not be profitable. It may impossible to specify a rate for which there is sufficient demand and incoming revenue to cover the development, marketing, and claims costs of the insurance and yield a net positive profit. In such cases, the insurer will opt not to offer coverage against this risk.
Mercury/Methylmercury Risk
Authors: Budtz‐jørgensen Esben , Grandjean Philippe
Published: September 2008

Mercury in the environment originates from both natural and anthropogenic sources, especially owing to energy production from fossil fuels. In sediments, inorganic mercury is converted into methylmercury, which is accumulated by fish, shellfish, and marine mammals, and attains its highest concentrations in large predatory species at the top of the food chain, e.g., pike, swordfish, and tuna, as well as shark, seals, and toothed whales. While the toxicity of mercury vapor and mercury salts has been known since ancient times, the risks due to environmental contamination with methylmercury came to the forefront when, about 1960, Minamata disease in Japan was found to be caused by methylmercury due to pollution from a local factory. The methylmercury poisoning in adults is characterized by paresthesias of the extremities, the tongue, and the face, followed by problems in motor coordination. Later, tunnel vision and hearing problems occur. In Minamata, Japan, it was noted that the pregnant mother could appear in good health, while her child would be born with a serious spastic paresis syndrome. Recent evidence from epidemiological studies has shown that subtle cognitive and motor function deficits have already begun to appear at exposure levels prevalent in some fishing communities. No effective antidote is available. Governmental agencies have issued advisories regarding consumption of freshwater fish and seafood, and an international agreement has been reached to limit mercury releases to the environment.
Public Health Surveillance
Authors: Stoto Michael A.
Published: September 2008

Public health surveillance seeks to assess and track the health of the public, define public health priorities, evaluate programs, and stimulate research. It includes case surveillance focuses on individuals, or sometimes groups of individuals, to identify individuals with certain diseases and take action to stop spread of disease. Statistical surveillance, on the other hand, focuses on populations to identify differentials and trends that can inform public health policymaking, including the allocation of resources. Surveillance systems rely on a diversity of data sources. Surveillance of infectious disease relies primarily on the system of notifiable diseases put in place in the nineteenth century. Statistical surveillance systems use data from vital statistics, population‐based sample surveys, registries, and health care records.

The introduction and growth of syndromic surveillance following 9/11 has focused on a statistical approach to detecting bioterrorist attacks. Despite many accomplishments, the value of syndromic surveillance for detecting bioterrorist attacks has not yet been demonstrated. Ultimately, the most important contribution of syndromic surveillance systems to public health may be for natural disease outbreaks, such as seasonal and pandemic influenza, and for other public health purposes beyond simple detection.