bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk Classification in Nonlife Insurance
Authors: Antonio Katrien , Beirlant Jan 
Published: September 2014

AbstractWithin 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

Abstract
Solvency is the ability of an insurance company to pay future claims. The required solvency capital is compared to available capital. The supervisor can impose the insurance company to act depending on the level of the available capital in comparison to the required capital. In a risk‐based system the required capital should be calculated including all quantitative risks and using a total balance sheet approach, i.e., recognizing both the asset and liability sides of the balance sheet, as well as the interdependence between them. A pragmatic solution to nonnormality and nonlinearity is discussed.
Adverse Selection
Authors: Kunreuther Howard 
Published: September 2014

Abstract

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

Abstract

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

AbstractThis 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

AbstractDecision 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.
Influence Diagrams
Authors: Smith Jim Q. , Thwaites Peter 
Published: September 2014

AbstractInfluence 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

Abstract
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.
Availability and Maintainability
Authors: Kumar Uday , Akersten Per Anders 
Published: September 2014

Abstract
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

Abstract
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

Abstract
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

Abstract

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.
Reliability Demonstration
Authors: Mease David , Nair Vijayan N. , Robinson Jeffrey A. 
Published: September 2014

Abstract
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.
Recurrent Event Data
Authors: Stocker Russell S. 
Published: September 2014

Abstract

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

AbstractCompeting 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

Abstract
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

AbstractDuring 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

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

Abstract
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.
Combining Information
Authors: Piegorsch Walter W. , Bailer A. John 
Published: September 2014

Abstract

An area of growing importance in quantitative risk analysis concerns the combination of information from diverse sources. A common rubric for combining the results of independent studies is
meta‐analysis
. The goal of the methodology is to bring together results of different studies, reanalyze the disparate results within the context of their common endpoints, increase the sensitivity of the analysis to detect the presence of adverse effects, and provide a quantitative analysis of the phenomenon of interest based on the combined data. This entry discusses some basic methods in meta‐analytic calculations, and includes commentary on how to combine or average results from multiple models applied to the same set of data.
Geographic Disease Risk
Authors: Wakefield Jon 
Published: September 2014

Abstract
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

AbstractIn 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract

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.