bluuebunny/crossref_metadata_2025_split huggingface dataset:
Operational Risk Modeling
Authors: Cruz Marcelo 
Published: September 2008

Abstract
Modeling operational risk is a key challenge for the financial industry particularly after the Basel Committee issued the New Capital Accord in June, 2004 that established a capital charge for this risk for the first time. Although there are three methods for risk measurement allowed by the regulators (the basic indicator, the standardized approach, and the advanced measurement approach), most large institutions are going in for the advanced approach, which implies the development of their own framework for risk measurement and capital allocation. Despite the fact that the New Capital Accord was very strict on the rules for the data inputs to the model, it was also very broad in terms of which model should be used for measuring operational risk. In this article, the concept of modeling an operational risk database is explained and a framework based on statistical and mathematical approaches to measure and manage this risk is delineated.
Arsenic
Authors: Slayton Tracey M. , Lewis Ari S. , Beck Barbara D. 
Published: September 2008

AbstractArsenic 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

AbstractThis article reviews the scientific evidence and methodologies that have been used to assess the human health risks posed by 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin (TCDD). It also presents a probabilistic analysis for identifying virtually safe concentrations of TCDD toxicity equivalents (TEQ) in residential soils. Updated data distributions that consider state‐of‐the‐science cancer and noncancer toxicity criteria, child soil ingestion and dermal uptake, bioavailability in soil, and residential exposure duration are incorporated.
Design of Reliability Tests
Authors: Kececioglu Dimitri B. , Chen Xiaofang 
Published: September 2008

Abstract
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

AbstractReliability 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

Abstract
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

AbstractPrecise 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

AbstractThe 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

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 2008

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.
Lifetime Models and Risk Assessment
Authors: Lee Mei‐Ling Ting 
Published: September 2008

AbstractIn 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.
Condition Monitoring
Authors: Banjevic Dragan , Jardine Andrew K.S. 
Published: September 2008

Abstract

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

AbstractThe 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

Abstract

The
ALARP principle
and the closely related
ALARA
(as low as reasonably achievable)
principle
, refer to the notion that risks should be “as low as reasonably practicable”. This principle is applied, particularly in the United Kingdom, in risk regulation. Broadly speaking, activities involving high risks will not be allowed, activities with negligible risks will not be subject to restriction, while those activities in between will be subject to cost–benefit‐based risk reduction. The paper discusses the following topics: the principles of ALARP, in particular, the notion of disproportion; how risk is measured, in particular, the notions of individual and societal risk; the concept of tolerable risk arising in the UK Sizewell B Enquiry; how the cost–benefit trade‐off is made, including valuations of a statistical life; criticism of the ALARP principle; and an example.
Bayesian Statistics in Quantitative Risk Assessment
Authors: Congdon Peter 
Published: September 2008

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.
Behavioral Decision Studies
Authors: Brandstätter Eduard , Riedl René 
Published: September 2008

Abstract
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

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.
Expert Judgment
Authors: Hora Stephen C. 
Published: September 2008

AbstractExperts 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

AbstractThe practice of risk assessment depends heavily on insights from the theory of the individual decision maker, both normative and behavioral. However, most risk related decisions impact on, and involve, multiple parties who may have interests which are not congruent. This article considers what light normative and behavioral theories of group decision can shed on risk assessment problems.This article reviews ideas from game theory, such as the Nash equilibrium and the Nash bargaining solution, and relates them to a nuclear‐waste management example. Key ideas from the theory of social choice, such as Arrow's Paradox and Sen's Impossibility of a Paretian Liberal are also discussed. Alongside this normative theory, some of the key findings from the social psychological literature about how preferences are formed in groups are also described, focusing particularly on theories of social influence and phenomena such as polarization and groupthink.The theories of group decision alert the analyst to ways in which group decisions can be badly made: they may be unfair, unsustainable, arbitrary, or based on poorly shared information, or deliberately or accidentally suppressed dissent. Fortunately, it is possible to design decision processes which make appropriate use of risk and decision analytic technologies, but which are also responsive to the social context in which the decision takes place.
Influence Diagrams
Authors: Smith Jim Q. , Thwaites Peter 
Published: September 2008

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.
Interpretations of Probability
Authors: Lenk Peter 
Published: September 2008

Abstract
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

Abstract
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

Abstract
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

AbstractIncreasingly, 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

AbstractUncertainty 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.