bluuebunny/crossref_metadata_2025 huggingface dataset:
Geographic Information Systems
Authors: Griffith Daniel A.
Published: September 2014

A geographic information system (GIS) is a computer hardware and software information system designed to capture, edit, manage, house, manipulate, analyze, and display georeferenced data. It comprises high‐resolution scientific visualization capabilities, large‐capacity electronic storage devices, efficient and effective structures for data storage and retrieval, high‐volume communication channels, specialized algorithms for data integration and reliability analysis, and specialized query languages. Its reference point is a digital map. The first GIS was developed by the Canadian government and implemented in 1964. A differentiating feature among GISs is whether the underlying map involves a raster or vector surface partitioning. Spatial statistics requires attribute data, a map, and the tagging of each data observation to a location on a map, items furnished by a GIS. A GIS also supplies a practical and useful way to reveal spatial and temporal relationships among data. The UCGIS is an organization whose mission is to serve as an effective unified voice for the community of GIS users.
Geostatistics
Authors: Wackernagel Hans
Published: September 2014

Geostatistics is an application of the theory of random functions to spatially distributed data. The methods, initially proposed by Matheron in mining and petroleum exploration, found their way back to mainstream statistics more than a decade ago. The geostatistical methodology can be subdivided into linear and multivariate geostatistics, nonstationary geostatistics, nonlinear geostatistics, geostatistical simulation.
Financial Time Series
Authors: Tsay Ruey S.
Published: September 2014

Financial time series analysis is concerned with theory and practice of asset valuation over time. This article introduces key features and properties of financial time series and discusses econometric and statistical methods available for analyzing such series. It also discusses recent developments in financial time series analysis in the advent of high‐frequency transaction data and in estimation of stochastic diffusion equations for derivative pricing.
Nonlife Loss Reserving
Authors: Alba Enrique , Hernández Diego
Published: September 2014

This article presents a brief description of nonlife claims reserving methods. Nonlife claims reserves are financial resources, set aside from all other insurance operations, as a provision for claims not completely settled at a specific time. Since these provisions constitute the largest liabilities carried by nonlife insurers, their determination affects many aspects of the business, such as ratemaking and the assessment of the insurer's net worth. From the actuarial point of view, reserving is a problem of estimation, or more precisely, forecasting. As a consequence, reserving methods range from very simple deterministic techniques to some very sophisticated ones, based on stochastic models for claim settlement. We present, in a structured way, the main approaches in the field, and provide the interested reader with references for further study.
Risk Classification in Nonlife Insurance
Authors: Antonio Katrien , Beirlant Jan
Published: September 2014

Within the actuarial profession, a major challenge can be found in the construction of a fair tariff structure. We discuss botha priorianda posteriorirating systems in light of the statistical techniques that are involved. The article introduces basic concepts, illustrates them with real‐life actuarial data and summarizes references to complementary literature. Examples of likelihood‐based as well as Bayesian estimation are included.
Solvency
Authors: Sandström Arne
Published: September 2014

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.
Adverse Selection
Authors: Kunreuther Howard
Published: September 2014

If an insurer sets a premium based on the average probability of a loss in an entire population, those at higher‐than‐average risk for a certain hazard will benefit most from coverage, and hence will be the most likely to purchase insurance for that hazard. In an extreme case, the poor risks will be the only purchasers of coverage, and the insurer can expect to lose money on each policy sold. This situation, referred to as
adverse selection
, occurs when the insurer cannot distinguish between members of good‐ and poor‐risk categories in setting premiums. This article discusses the implications of adverse selection for insurance.
B
ayes' Theorem and Updating of Belief
Authors: Lee Peter M.
Published: September 2014

The conditional probability of an event
A
given an event
B
is defined as Pr(
A
 ∩ 
B
)/Pr(
B
). If you start with hypotheses
H

i

for which your initial assessments of probability (your
prior probabilities
) are Pr(
H

i

) and you then observe data, the probability of which is dependent on which of the hypotheses is true (supposing them to be exclusive and exhaustive, so that one and only one of them is true), then
Bayes' theorem
allows you to update your assessments of probability to
posterior probabilities

Sometimes we have an unknown parameter that can take any value in a continuous range and the observation we are thinking of can similarly take values from a continuous range. In this case, we get Bayes' theorem for random variables in the form

These formulae can be used sequentially, taking the posterior form after one or more observations as the prior for the next.
Bayesian Statistics in Quantitative Risk Assessment
Authors: Congdon Peter
Published: September 2014

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.
Decision Analysis
Authors: French Simon
Published: September 2014

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.
Decision Modeling
Authors: Smith Jim Q. , Thwaites Peter
Published: September 2014

Bayesian decision analysis provides a coherent and logical methodology for not only the expression and exploration of potential risks under uncertainty, but also a framework through which to discover an optimal course of action. Uncertainties are expressed probabilistically and optimal acts discovered through maximizing the decision maker's expected utility. The basic methodology is illustrated by a simple example of agricultural risk management.
Influence Diagrams
Authors: Smith Jim Q. , Thwaites Peter
Published: September 2014

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.
Multiattribute Utility Functions
Authors: Stewart Theodor J.
Published: September 2014

The multiattribute utility function is a means of systematically analyzing preferences in the context of multiattribute or multicriteria decision making under conditions of uncertain or risky outcomes, and thus integrates concepts from multiattribute value measurement and expected utility theory. The process starts by eliciting utility functions for each individual attribute in turn, which then have to be aggregated in some sense in order to form the overall multiattribute utility function. A weighted additive aggregation is simple, but is demonstrated to force certain preference properties that may not be justifiable in certain decision contexts. For this reason, weaker sets of assumptions have been developed, which lead either to multiplicative or multilinear aggregation rules. Unfortunately, the weaker the sets of assumptions made, the more the decision maker is required to make cognitively challenging judgments between multivariate lotteries.
Distributions for Loss Modeling
Authors: Panjer Harry H.
Published: September 2014

We illustrate families of continuous and discrete distributions commonly used in modeling the size and number of (nonnegative) losses in applications including operational risk management and insurance.
Parametric Probability Distributions in Reliability
Authors: Coolen Frank P.A.
Published: September 2014

In this paper, we present an overview of basic parametric probability distributions that are frequently used in reliability. We present some main characteristics of these distributions, and briefly discuss underlying assumptions related to their suitability as models for specific reliability scenarios.
Availability and Maintainability
Authors: Kumar Uday , Akersten Per Anders
Published: September 2014

The increasing pace of mechanization and automation of industries has enhanced the business risks associated with nonfunctioning of production units and other engineering systems. In general, such units and engineering systems are expected to function round the clock without failure. However, owing to design deficiencies or the influence of operational stresses, these systems are not always able to meet the users' requirements in terms of system performance. This is often attributed to poorly designed reliability and maintainability characteristics, combined with poor maintenance and product support strategies. If the designed reliability and maintainability characteristics are poor and the maintenance organization is unable to meet the requirements, this may result in a poor availability performance. Even though high reliability is desirable to reduce the costs of maintenance and maintenance preparedness, the benefits of improved maintainability should not be ignored in achieving the required availability performance. Availability is the most commonly used measure of the state of plant and systems all over the world.

In this chapter, a brief discussion on the concepts of availability and maintainability is presented with some examples.
Reliability Integrated Engineering Using Physics of Failure
Authors: Wang Weiqiang , Das Diganta , Osterman Michael , Pecht Michael
Published: September 2014

Reliability is the ability of a product to perform without failure and within specified performance limits, for a specified time, in its life‐cycle application conditions. Physics of failure is the preferred methodology to design for and assess product reliability. This article presents the concept and underlying principles of physics of failure and shows how the methodology is used as an integrated product development method to develop, assess, and enhance product reliability.
Stress Screening
Authors: Newman Robert M.
Published: September 2014

Stress screening is a process used by manufacturers to minimize the risk of delivering products with latent defects that may cause failure in service. It is particularly applicable to low‐volume products with manual intervention in final assembly and where the consequences of service failure can be severe. This article explains the background to stress screening and how to go about determining the need for and establishing the test conditions for maximum benefit.
Near‐Miss Management: A Participative Approach to Improving System Reliability
Authors: Oktem Ulku , Meel Anjana
Published: September 2014

Investigations of many serious accidents in different fields have revealed that before every serious accident, a large number of related prior incidents occurred with limited impact and an even larger number of related incidents happened that resulted in no loss or damage. Collectively, these incidents are called
near‐misses
. Near‐misses provide insight into both potential failure points and weaknesses in the management system itself. Therefore, they can be a powerful tool to reduce risk and improve system reliability. Near‐miss management systems (NMMS)s are designed to enable people and institutions to learn from high‐frequency, low‐impact incidents (near‐misses) to prevent low‐frequency, high‐impact events (accidents). A comprehensive NMMS includes several important implementation steps, such as identification of near‐misses, disclosure and reporting, prioritization and classification, distribution of the information, analysis of causes, solution identification, dissemination of actions and knowledge, resolution, and closure of the case. Successful implementation of an NMMS requires (a) strong management ownership, (b) participation and reporting of as many incidents as possible, and (c) use of quantitative tools to identify weaknesses and to improve the system.
Software Testing and Reliability
Authors: McGettrick Andrew D.
Published: September 2014

Software, increasingly, forms an important part of many systems—engineering, medical, administrative, educational, finance, and so on. Achieving high level of reliability of the software is a prerequisite for that system being successful. This article addresses the problem of testing of software and relates it to issues about reliability.
Reliability Demonstration
Authors: Mease David , Nair Vijayan N. , Robinson Jeffrey A.
Published: September 2014

Reliability demonstration test plans are used in industry to verify that the reliability of a product meets a specified target with a certain degree of confidence. This article provides an overview of reliability demonstration plans from a statistical perspective and gives some examples for illustration. Bayesian techniques and extreme testing, two general approaches for increasing the efficiency of test plans and reducing sample sizes, are also discussed.
Multivariate Reliability Models and Methods
Authors: Hanagal David D.
Published: September 2014

We propose some multivariate reliability models with emphasis on quantitative risk assessment. We give a brief description of different exponential and Weibull models used for multicomponent systems and frailty models for multivariate survival data. We give some applications of frailty models for the multivariate survival data.
Recurrent Event Data
Authors: Stocker Russell S.
Published: September 2014

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 2014

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.
Zero Failure Data
Authors: Atwood Corwin L. , Vaurio Jussi K.
Published: September 2014

Estimating failure probabilities or risk when no failures have been observed is an inherently difficult, but not uncommon task. Several approaches are described in this article.

Be content with a “pessimistic” estimate—an upper bound or a conservative distribution. This can be a confidence limit, or the results from a Bayesian distribution whose percentiles match the confidence upper limits, or the results based on a “noninformative prior”.

Find data for similar equipment, and construct an estimate with the aid of the other data. Empirical Bayes or hierarchical Bayes methods may be appropriate. (This method is not limited to zero failure data.) If one must fall back on engineering judgment, a “constrained noninformative prior” may be helpful.

Analyze the parts, ways, or processes by which the system can fail, and use data on these various parts to construct an estimate for the system as a whole. Several examples are mentioned, including structural reliability and various segments of probabilistic safety assessment (PSA).

If a few failures have been observed instead of no failures, some aspects of the situation are greatly improved: we no longer need to be content with an upper bound, and Bayesian results are much less sensitive to details of the prior distribution.
Credit ScoringviaAltmanZ‐Score
Authors: Altman Edward I. , Chan Ngai H. , Wong Samuel Po‐Shing
Published: September 2014

During the last several years, assessing the probability of default (PD) and loss give default (LGD) on credit assets have become a high priority task in the internal credit risk systems of the financial institutions. Such an intense surge of interest is caused by the following factors:

A historic high in the frequency and severity of bankruptcy at the turn of the century have attracted much media attention.

Basel Accord on credit risk capital adequacy—the so‐called Basel II Accord; see Basel Commission on Banking Supervision—for banks and financial institutions to qualify for the advanced internal rating based (IRB) approach, they need to develop better assessment of PD and LGD.

To address the enormous need of PD and LGD evaluations, various quantitative techniques have been employed not only by financial institutions, but also regulatory bodies and the rating agencies. In this chapter, a brief review of the techniques of credit scoring in estimating PD is presented. For LGD computations, one can refer to Chapter 15 of Altman and Hotchkiss.
Meta‐Analysis in Clinical Risk Assessment
Authors: Goldberg Judith D. , Watson Heather N. , Friedman Herman P.
Published: September 2014

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.
Efficacy ‐ Clinical
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana
Published: September 2014

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.
Benchmark Dose Estimation
Authors: Al‐Saidy Obaid M.
Published: September 2014

The benchmark dose (BMD) methodology is a statistical procedure, first proposed by Crump in 1984, to determine allowable daily intakes of chemicals. The BMD was proposed as an alternative to the no‐observed‐adverse‐effect‐level (NOAEL) approach. The procedure is now widely used in risk assessment to establish health standards and guidelines for exposure of toxins to humans or to the environment. In this article, we review the BMD procedure, give details on how to compute the BMD and BMDL for both quantal and continuous endpoints and survey some of the recent developments in benchmark dose estimation.
Canonical Modeling
Authors: Voit Eberhard O.
Published: September 2014

In contrast to probabilistic risk assessments in industry, environmental and health risk assessments require dynamic models that capture the essence of complex biological systems. Canonical models withinbiochemical systems theoryare particularly well suited, as they can represent any degree of complexity, yet allow for streamlined mathematical and computational analyses.
Combining Information
Authors: Piegorsch Walter W. , Bailer A. John
Published: September 2014

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.
Geographic Disease Risk
Authors: Wakefield Jon
Published: September 2014

The examination of disease risk summaries across space has a long history in epidemiology and may be performed for disease mapping, cluster detection, examination of clustering, or spatial regression. Often these exercises are carried out to gain clues to etiology with respect to putative environmental exposures. The spatial nature of the data has positive and negative aspects. For disease mapping, the spatial aspect can be exploited, since one expects near‐by areas to be more similar in terms of their risk. For spatial regression, the analysis is complicated by the spatial aspect because the usual independence assumption of conventional analysis methods is violated, and there may also be “confounding by location”. As in all epidemiological investigations, interpretation depends crucially on issues of data quality, in particular, errors in population and disease counts and exposure misclassification. Ecological bias, which arises when aggregate associations are assumed to apply at the level of the individual, is also an issue that requires serious consideration.
Cost‐Effectiveness Analysis
Authors: Fenwick Elisabeth A.L. , Briggs Andrew H.
Published: September 2014

In a resource‐limited environment, it is important to determine whether a proposed health program represents a good use of scarce resources (money, labor, land, capital, etc.). Economic evaluation provides a framework to approach and inform difficult decisions about the best use of limited resources, through an assessment of both the costs and outcomes associated with a proposed program. This article first examines the foundations of economic evaluation in welfare economic theory and cost‐benefit analysis. The article then moves quickly on to a presentation of cost‐effectiveness analysis incorporating a discussion of incremental cost‐effectiveness decision rules, methods for handling uncertainty, and the use of value of information for decision making. The article focuses on the requirements of a health care policy maker attempting to determine whether to reimburse a health care technology given the information and uncertainty surrounding the decision. In addition, the article examines how the policy maker might approach the choice about whether to request further research to inform the decision. A stylized case study is used to illustrate the concepts and approach. The article links with those on decision analysis, uncertainty analysis, and value of information analysis.
Linkage Analysis
Authors: Shao Yongzhao
Published: September 2014

Linkage analysis is an important initial step in localizing and identifying genes in the chromosomes that underlie many human diseases and other traits of interest. Frequently, linkage analysis proceeds by comparing the inheritance pattern of the trait and that of the genetic markers to establish linkage between the trait and marker loci. This article provides a brief overview of commonly used methods for linkage analysis, which include model‐free and model‐based methods for mapping qualitative‐ and quantitative‐trait loci. Issues on the design of linkage studies are also briefly discussed.
Risk Characterization
Authors: Rai Shesh N.
Published: September 2014

Probabilistic risk characterization is now commonly used to estimate and predict risk caused by exposure to unwanted substances. We characterize risk as a function of risk factors and discuss two examples: one is based on a multiplicative model of contaminated drinking water exposure and the other is based on a general model of radon exposure. The risks associated with contaminated drinking water are easy to characterize and understand; however, those associated with radon exposure are not as well understood as the former. Radon exposure is an established risk factor for human lung cancer, and these data are used to project radon‐associated lung cancer risks to the general population.
Uncertainty and Variability Characterization and Measures in Risk Assessment
Authors: Rai Shesh N.
Published: September 2014

Probabilistic risk characterization, which is now commonly used to model risk and exposure, involves identifying sources of variations. We characterize risk as a function of risk factors. The distribution of any risk factor depends on two sources of heterogeneity (dispersion): uncertainty and variability. Uncertainty represents the degree of ignorance about the precise value of a particular parameter of a distribution. Variability represents inherent variation within the population of interest. We give an example to demonstrate uncertainty and variability that is based on a multiplicative model of risk due to contaminated drinking water.
Attributable Fraction and Probability of Causation
Authors: Greenland Sander
Published: September 2014

This article discusses the concepts and definitions associated with terms such as attributable fraction, attributable risk, and probability of causation. It is emphasized that there are several incompatible definitions of attributable risk. Some of these definitions apply to concepts of excess caseload and excess rate, which are estimable from epidemiologic data; others apply to concepts of etiologic fraction and probability of causation, which are not estimable without strong assumptions about biologic mechanisms of disease.
Confounding ‐ Structure
Authors: Hernán Miguel A.
Published: September 2014

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.
Causal Diagrams
Authors: Greenland Sander , Pearl Judea
Published: September 2014

From 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

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.
Early Warning Systems (EWSs) for Predicting Financial Crisis
Authors: Yung Sunny W.S.
Published: September 2014

Research 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.
Numerical Schemes for Stochastic Differential Equation Models
Authors: Higham Desmond J. , Kloeden Peter E.
Published: September 2014

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.
Professional Organizations in Risk Management
Authors: Apostolik Richard
Published: September 2014

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.
Default Risk
Authors: Dowd Kevin
Published: September 2014

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.
Extreme Value Theory in Finance
Authors: Brodin Erik , Klüppelberg Claudia
Published: September 2014

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 2014

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

In 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

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

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

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

This 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

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

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

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

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

This 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

We 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.