bluuebunny/crossref_metadata_2025 huggingface dataset:
Coding: Statistical Data Masking Techniques
Authors: Slavkovic Aleksandra B.
Published: September 2014

Coding broadly refers to altering the data in some way. In computer science literature, it may refer to encryption, compression, or error correction. These are relevant for cryptography and cryptanalysis, and for data privacy and homeland security. In statistical confidentiality literature, coding encompasses various data‐masking techniques. These techniques generally introduce bias and variance to data. The question is how much and what kind of bias and variance is introduced and how this influences the utility of data‐mining algorithms and precision of identification, that is the false positive error rate. This article gives a brief overview of statistical disclosure limitation techniques for data masking, pointing out the likelihood of their use and potential impacts for data privacy.
Scenario‐Based Risk Management and Simulation Optimization
Authors: Better Marco , Glover Fred
Published: September 2014

Traditional optimization approaches for handling uncertainty and risk typically require severe assumptions that are often not satisfied in complex practical settings. In an effort to overcome such limitations, several methods have been developed to handle uncertainty when the data and associated real‐world parameters do not behave according to classical assumptions. Two of the leading and most widely used examples are scenario optimization and robust optimization, both of which seek high‐quality decisions that are feasible for all scenarios. However, both of these approaches likewise succumb to deficiencies encountered by classical methods, by exhibiting serious limitations in terms of the complexity and size of the models they can handle. Simulation optimization overcomes these limitations, and its flexibility and ease of use has contributed to its popularity as a preferred optimization approach to risk‐management applications.
Uncertainty Analysis and Dependence Modeling
Authors: Cooke Roger M. , Kurowicka Dorota
Published: September 2014

Uncertainty analysis involves quantifying (joint) uncertainty on the inputs of models, propagating this through the mathematical model, and interpreting the results. We sketch the emergence of uncertainty analysis, give recent examples, and briefly illustrate current active issues. Depencence modeling and probability inversion are high on the research agenda.
Engineered Nanomaterials
Authors: Maynard Andrew D.
Published: September 2014

Engineered nanomaterials are a product of nanotechnology, and have an intentionally produced structure at the scale of approximately 1–100 nm. These materials demonstrate properties that depend on a carefully controlled and implemented nanostructure; the close dependency between this structure and material functionality challenges how quantitative risk assessment is applied to some engineered nanomaterials. For instance, nanostructure‐related functionality may lead to health risks that depend on properties such as particle size, surface area, and shape. There is evidence that the size, surface area, surface chemistry, and possibly shape of some engineered nanomaterials may determine transport and response within the body. Quantitative risk assessment of these materials will depend on determining when they behave differently from conventional materials, what properties are associated with their potential impact, and how exposure, dose and response can be most appropriately evaluated. At present, insufficient data and incomplete methodologies are available for conducting comprehensive quantitative risk assessments on many emerging nanomaterials. Until more comprehensive information is available, it will be necessary to identify those nanomaterials that present unique challenges, and to use existing information together with expert extrapolation as a basis for qualitative and semi‐quantitative risk assessment.
Homeland Security and Transportation Risk
Authors: Steffey Duane L.
Published: September 2014

The coordinated hijackings and deliberate crashes of airplanes into the World Trade Center in New York and the Pentagon in Washington, DC, on September 11, 2001, dramatically heightened both the perception and the reality of the threat terrorism poses to public transportation systems. Governments of countries that perceive an elevated level of threat are investing considerable resources to implement comprehensive programs to enhance security in all key modes of transportation, including aviation, maritime traffic, highways, and mass transit.

A systems‐based approach to managing such risks involves the assessment of threats, vulnerabilities, and consequences. Assessments are conducted to identify and evaluate potential threats, recognize weaknesses that may be exploited by identified threats, suggest countermeasures to address those weaknesses, and determine which structures or processes are relatively more important to protect from attack. The characterization of security risk is completed through qualitative or quantitative analysis of the consequences associated with the partial or total loss of critical transportation assets because of intentional harmful attacks. Formal methodologies for assessing and managing risks to transportation security provide a conceptual structure and practical tools for allocating resources in cost‐effective ways to improve public safety.
Air Pollution Risk
Authors: Smith Richard L.
Published: September 2014

This article reviews the literature on health effects of air pollution, with a primary focus on time series studies but also covers prospective studies and case‐crossover designs. It also covers some of the controversies created by these studies and concludes with a brief review of air pollution standards in the United States.
Environmental Justice, Statistics in
Authors: Waller Lance A.
Published: September 2014

We briefly review the historical, legal, and regulatory background of environmental justice assessments, paying particular attention to the role of statistical techniques in such assessments. We stress the difference between “disparate impact” and intentional discrimination and the role of statistical comparisons as defined in the associated regulatory statutes. We contrast two general approaches, the first comparing demographic proportions within exposure categories and the second comparing exposure (or exposure surrogate) distributions between demographic categories.
Environmental Mutagenesis, Statistics in
Authors: Piegorsch Walter W.
Published: September 2014

In experiments studying the effects of environmental stimuli on biological organisms, major endpoints of interest include damage to genetic components such as deoxyribonucleic acid (DNA). DNA damage as represented by mutagenic insult(s) is perhaps the most varied of any toxicological endpoint, since the potential mechanisms of genetic damage are so numerous. Statistical issues associated with mutagenicity experiments include construction of models to characterize dose–response and other features of genetic damage, sample size determinations/study design, and the selection of statistical approaches associated with these models and methods; these are briefly discussed
Environmental Teratogenesis, Statistics in
Authors: Chen James J.
Published: September 2014

Teratology is the study of adverse effects of environmental agents on the developing system. The major source of information for studying potential teratogenic (developmental) effects of chemicals on humans is generally the animal dose–response experiments. Quantitative risk assessment involves an assessment of the relationship between exposure and adverse health effects to derive an allowable exposure level. Acceptable levels of human exposure are derived by dividing the lower confidence limit benchmark dose by the product of several uncertainty factors. This article describes the statistical models and benchmark approach for quantitative risk assessment of developmental effects. The benchmark dose method involves selection of a dose–response function to fit animal experimental data. The benchmark dose and its lower confidence limit are then estimated from the fitted dose–response model. The dose–response function for continuous response data is modeled using the litter‐based analysis. The dose–response function for dichotomous response data is modeled using beta binomial.
Extreme Event Risk
Authors: Brillinger David R.
Published: September 2014

“Extreme event” is a vague concept with a variety of formal definitions based on specific cases and contexts. Particular extreme events include, great earthquakes, floods, droughts, nuclear meltdowns, space shuttle losses, deaths, giant sea waves, stock crashes, landslides, storms, hurricanes, tornadoes, wildfires, and consequences of any of the preceding. The event of interest might be caused by man or by nature. They may occur in time or space. The size might be measured in deaths or costs. The study of extreme event situations has led to important research results and management methods. The specific concern of the entry is quantitative formulation of the problem.
Fate and Transport Models: Details
Authors: Pistocchi Alberto
Published: September 2014

In the following section we provide general background information about mathematical descriptions of phase partitioning of chemicals, mechanisms of their transfer between and within environmental media, and degradation, also introducing the most common mathematical representations of the processes described. Eventually, such representations are combined in a general equation used for fate and transport modeling: the advection–dispersion–reaction equation (ADRE). The processes described by the ADRE are very general; depending on the specific medium (soil, water, air) where they take place, the different terms of the equation are represented differently.

In a subsequent section, we describe specific cases of the ADRE in relation to surface water, soil/groundwater, and air, which are the domains where it is normally solved. For each case, we introduce some examples of publicly available computing codes that solve the equation.

In the last section, we introduce multimedia/multispecies fate and transport of chemicals using linear box models.
Gene‐Environment Interaction: Overview
Authors: Umbach David M.
Published: September 2014

Current biomedical research is guided by the idea that genetic makeup and environmental exposures, broadly construed, work together through time to cause human disease. This idea is the broad essence of gene–environment interaction. The detailed assessment of gene–environment interaction using epidemiologic data involves issues such as how one defines such interactions in a statistical sense and how one might infer that interactions seen in observational studies reflect causal relationships. That gene–environment interaction is hard to detect and estimate, requiring much larger sample sizes than main effects, has prompted research into novel approaches to increase power and precision through both methods of data analysis methods and study designs. Efforts to make studies robust against hidden genetic population structure have prompted other innovations in study design. The needs of biomedical science continue to stimulate research toward improved methods for studying gene–environment interaction.
Global Warming, Statistics for
Authors: Tebaldi Claudia , Mastrandrea Michael D. , Smith Richard L.
Published: September 2014

Global warming is a worldwide environmental, social, and economic challenge, with far‐reaching implications for human welfare and the health of natural systems. While this qualitative assessment is generally agreed upon by a large share of scientific, policy‐making, and stakeholder communities, a quantification of the risks associated with global warming is far from straightforward. There exist considerable uncertainties, including limits to predicting future changes in the drivers of greenhouse gas emissions (population, economic growth, etc.), incomplete scientific understanding of the response of the climate system to those emissions, and components of intrinsic randomness or unpredictability in the processes involved. From the size of the changes to be expected to their timing and from the significance of impacts to the costs of adaptation and mitigation strategies, every link of the chain from emissions, to impacts, to the effects of policy is affected by uncertainties. This article characterizes the main sources of uncertainty in modeling of the climate system and in the development of scenarios for future emissions, as well as the most rigorous efforts currently documented in the peer reviewed literature to quantify uncertainty and incorporate it into risk analysis.
Hotspot Geoinformatics
Authors: Patil Ganapathi P. , Joshi Sharadchandra W. , Rathbun Stephen L.
Published: September 2014

Government agencies continue to require meaningful summaries of georeferenced data to support policies and decisions involving risk maps, geographic targets, and resource allocations. So also the public with initiatives of digital governance, enabling transparency, efficiency, and efficacy in the risk assessment and management.

This article briefly introduces hotspot geoinformatics for hotspot detection and prioritization, and provides examples of societal importance involving environmental risk.
Potency Estimation
Authors: Bailer A. John , Piegorsch Walter W.
Published: September 2014

A common goal in environmental risk assessment is description of the hazardous agent's toxic or otherwise‐harmful effects, often for comparison with others agents of related structure or exposure. Summary measures are most useful in this regard, as these describe the potency of an agent or some product of an agent on the organism or system under study. A number of quantitative summaries can be used to measure or define potency, each based on a slightly different feature of the observed dose–response. We consider a few of these in this article.
Precautionary Principle
Authors: Grandjean Philippe , Keiding Niels
Published: September 2014

The
precautionary principle
is a term used about a set of statements all expressing the general intention that, in environmental risk policy, it is prudent to not always wait for definitive proof of harm before taking regulatory action. We present the concept and outline its relation to scientific discovery and statistical uncertainty.
Environmental Remediation, Statistics in
Authors: Stein Alfred , Kerle Norman
Published: September 2014

This entry gives an overview of environmental remediation, with an emphasis on soil issues. The concept of risk is defined, followed by different ways of modeling (deterministic, probabilistic, and hybrid). A distinction is made between
in situ
and
ex situ
remediation and issues of spatial statistics are included. The issue is illustrated with an example on an environmental cost model.
Spatial Risk Assessment
Authors: Lawson Andrew B.
Published: September 2014

The spatial analysis of risk is defined as the analysis of georeferenced data pertaining to some adverse outcome. Approaches used for this usually lie in the area of clustering methodology either as hypothesis tests or modeling of risk itself. For fixed spatial sites, and clustering around these, there are well‐developed methods related to distance and direction that can be used. When a focus site is not available, more sophisticated models or scan tests must be employed.
Spatiotemporal Risk Analysis
Authors: Lawson Andrew B.
Published: September 2014

This article focuses on the issues related to risk assessment when the domain consists of space and time. Spatiotemporal risk assessment could involve separate analyses of risk in a geographical domain, risk in a temporal window, or the interaction of space and time. Often the focus will be on magnitude and frequency of an outcome but this will have a spatial and temporal expression. The link with surveillance for adverse outcomes is made, and health risk is a focus of the review, although the concepts illustrated can be applied in other areas such as financial or industrial risk analysis.
Public Participation
Authors: Bayley Clare L.
Published: September 2014

Increasingly, governments and public bodies are involving stakeholders and the general public to a far greater extent in decision‐making processes, particularly relating to the environment in which they live. A strong motivation for the uptake of participatory processes is a need for transparency and accountability in decision making. Additionally, agreements such as the Aarhus convention granting the public rights regarding access to information, public participation, and access to justice in governmental decision making on matters concerning the environment have led to an increase in stakeholder participation. Many participatory tools and strategies have been developed to involve the public and stakeholders in societal decisions, including workshops, citizen juries, focus groups, electronic forums, web polling, and other means of consultation. In this article, the objectives of public participation are discussed and the merits of a number of participatory processes at meeting these objectives are evaluated.
Risk Attitude
Authors: Munier Bertrand R. , Tapiero Charles
Published: September 2014

The concepts and analytical expressions of absolute and relative risk aversion, in both weak and strong senses, are explained and discussed. Examples are computed. The extension to multivariate risk aversion, the problem of background risk, and the relationship between mean‐variance and expected utility (EU) models are dealt with in an EU framework. Finally, new developments in relation to the question of generalized risk aversion in a non‐EU framework and to the measurement of risk aversion concepts in financial analysis are discussed.
Ethical Issues in Using Statistics, Statistical Methods, and Statistical Sources in Work Related to Homeland Security
Authors: Seltzer William , Anderson Margo
Published: September 2014

The article emphasizes that there are distinctive ethical, as well as scientific and legal, issues involved in deploying statistical methods and data for homeland security, even as we note the overlaps between science and ethics on the one hand and law and ethics on the other hand. The article discusses (a) ethical challenges that arise from using inappropriate methods, whether they are “quick‐fix” shortcuts and approximations or elaborate models; (b) the ethical imperative to avoid causing harm to innocent people embodied in the need to respect the principle of statistical confidentiality and the need to take into account the intermediary and long‐term substantive objectives of any risk assessment; and (c) the common ethical failure involved in using only the technical indicator, “risk of disclosure”, rather than its ethically informed counterpart “the harm arising, given the disclosure”, in the assessment of the efficacy alternative approaches. The special ethical challenges that arise when data systems are used to target individuals or population subgroups based on racial, ethnic, religious, or similar attributes are also discussed. The article concludes by stressing the importance of establishing a mechanism for appropriate ethical review and oversight lest, in our untempered zeal for the value of the data and methods we employ or the important objectives of work on homeland security, we become complicit in harming innocent individuals or vulnerable population subgroups.
Expert Elicitation for Risk Assessment
Authors: Wiedlea Andrew C.K.
Published: September 2014

Expert elicitation—the use of structured, calibrated questions to gather qualitative risk information—is a key risk assessment tool. Particularly when there does not exist sufficient observed data to estimate the nature of a risk process, or in cases when a system risk is generated by the interactions of a complex set of component interactions, qualitative information gathered from subject matter experts may be the best, if not the only, source of relevant information. Ensuring that information gathered from subject matter experts is relatively free from bias and probability logic inconsistency is a key interest of elicitation research.
Scientific Uncertainty in Social Debates Around Risk
Authors: Driedger S. Michelle
Published: September 2014

Quantitative risk assessments based on scientific understandings of a risk issue are often used in managing risk controversies in a policy domain. However, in several areas of risk, particularly in an environment and health domain, managing uncertainties in scientific knowledge is difficult and complex. It is important to understand how the production and use of scientific evidence can be understood as a social process and how scientific evidence may be used in regulatory decision making.
Risk and the Media
Authors: Mistry Bhavnita , Driedger S. Michelle
Published: September 2014

The news media has long been recognized as an important social player in civil society in shaping public discourse concerning current events, particularly in the communication of science, policy, and risk issues. While the media may not tell us what our thoughts should be, it has considerable power in influencing the same by virtue of what it does, and does not, report. This article explores how the media report and frame risk issues.
Conflicts, Choices, and Solutions: Informing Risky Choices
Authors: Ricci Paolo F.
Published: September 2014

This article deals with key aspects of informing decision making of what may be the optimal choice, given a set of potential choices, unpredictable events, the value of the consequences (positive or negative) associated with each choice, and criteria for making the choice. This article introduces some of those criteria, methods and measures that can be used to inform policy maker relative to the set of options included in the analysis, and provides examples.
Simulation of the Beta‐Stacy Process
Authors: Blasi Pierpaolo
Published: September 2014

This article reviews simulation methods for the beta‐Stacy process, which belongs to the family of neutral‐to‐the‐right (NTR) processes used in Bayesian nonparametric inference on survival distributions. The definition and properties of the NTR processes are recalled, with a view to represent the continuous component of the process at the integrated hazard levelviathe Lévy measure. The conditional distribution of the prior process, given right‐censored data, is described; this represents the key ingredient for devising sampling schemes. Four methods aiming at the generation of increments or trajectories of the hazard rate process are revised. We explain the ideas behind each of them and sketch the correspondent algorithms for the posterior beta‐Stacy process.
Quadrature and Numerical Integration
Authors: Ausín M. Concepción
Published: September 2014

The objective in numerical integration is the approximation of a definite integral using numerical techniques. There is a large number of numerical integration methods in the literature and this article overviews some of the most common ones, namely, the Newton–Cotes formulas, including the trapezoidal and Simpson's rules, and the Gaussian quadrature. Different procedures are compared and illustrated with examples. Discussions about more advanced numerical integration procedures are also included.
M
arkov Chain
M
onte
C
arlo, Introduction
Authors: Wiper Michael P.
Published: September 2014

In this article, we introduce the basic ideas of Markov chain Monte Carlo simulation. We start by briefly commenting on the main ideas of Monte Carlo sampling and then by reviewing the theory of Markov chains, concentrating in particular on the conditions required for the existence of a stationary distribution. We then introduce the Metropolis–Hastings algorithm and show that this generates a Markov chain with a specific stationary distribution. We next introduce some specific ways of implementing the Metropolis–Hastings algorithm,
via
the independence sampler, the Metropolis sampler and the Gibbs sampler among others. Finally, we comment on how the convergence of a Markov chain to equilibrium can be assessed in practice and we finish with an example.
Decision Search via Simulation
Authors: De Iorio Maria
Published: September 2014

This entry provides an overview of simulation‐based methods in decision theory and optimal design problems. We consider decision problems defined by a utility function and an underlying probability model for all unknowns. The search for the optimal design requires the maximization of the expected utility with respect to some design parameter, where the expectation is taken with respect to all the unknowns. In many applications the solution to the optimal design problem is not analytically tractable, as neither the maximization nor the integration have a closed‐form solution requiring approximation and/or simulation‐based methods. We present four related simulation‐based strategies: prior simulations, curve fitting of Monte Carlo experiments, Markov chain Monte Carlo simulations in an augmented probability model, and inhomogeneous Markov chain simulations.
Perfect Sampling
Authors: Connor Stephen B.
Published: September 2014

Perfect simulation algorithms provide a method for generating a
perfect
sample from the stationary distribution of an ergodic Markov chain. We introduce two such algorithms, CFTP and domCFTP, and demonstrate their use with some simple examples.
Lévy Processes, Simulation of
Authors: Rosiński Jan
Published: September 2014

Simulation methods of Lévy processes based on random walk approximation, series representations of Lévy processes, and Poisson and Gaussian approximations are reviewed. Formulas for simulation of some Lévy processes are given as examples.
Dependence
Authors: Shore Haim
Published: September 2014

Dependence and independence are addressed from three perspectives: definition, relationship between dependence and correlation, and the expected value of the product of univariate functions of independent random variables.
Descriptive Statistics
Authors: Shore Haim
Published: September 2014

Descriptive statistics, calculated from the data, provide summary information about properties of a data set. In this article some of the most widely used descriptive statistics are detailed with the allied formulae. These include the sample average, median and other sample quantiles, mode, standard deviation, coefficient of variation, range, interquartile range, skewness, and kurtosis.
Pooled Variance, Pooled Estimate
Authors: Bucchianico Alessandro Di
Published: September 2014

Pooled estimators are estimators that combine the statistical information from different samples.
Counts, Rates, and Proportions, Statistical Methods for
Authors: Thyregod Poul
Published: September 2014

Poisson distribution and multinomial distribution are natural distribution models for count and frequency data for categorical variables. When there are only two categories, the multinomial reduces to the binomial distribution.

The distributions are skewed with a variance that changes with the mean value, and therefore standard methods for normally distributed variables such as ANOVA (analysis of variance) and the general linear model cannot reasonably be applied.

When events are counted and classified by two or more categorical variables or when sample units are classified, the counts or frequencies are often presented in tabular form in so‐called contingency tables.

A general framework for analyzing association in multidimensional contingency tables is provided by the log‐linear models. When a set of nested models is formulated, higher‐order interaction terms may be removed successively in analogy with ANOVA models for normally distributed variables. A subset of these models, the logit models, may be used for assessing the effect of explanatory variables on a categorical response variable.

Another important subset of the log‐linear models is constituted by the graphical models. These models allow for a factorization of the joint probability into a product of conditional distributions.

The log‐linear and logit models are special cases of a wider class of models, the generalized linear models. In this framework, the effect of continuous explanatory variables may be modeled in regression‐like models. An example is the logistic regression where the effect of explanatory variables on a response probability is modeled.

When the assumption of independence is violated, overdispersion might result. A method for modeling overdispersion is also presented.
Measures of Association
Authors: Hershberger Scott L. , Fisher Dennis G.
Published: September 2014

Measures of association are numerical estimates of the magnitude of the statistical dependence between two variables, especially if these variables are categorical. A multitude of such measures have been proposed; we review some of the most commonly used ones, dividing them into two types. The first is suitable for nominal scale data, and includes measures of predictive association. The second type is suitable for ordinal data, and includes measures of concordance‐discordance.
Estimation: General Aspects
Authors: Van De Geer Sara A.
Published: September 2014

This paper is concerned with the relationship between sample data and the probability distribution (
P
), which is supposed to have generated it. The estimation of
P
using an empirically derived distribution is discussed along with specific estimators such as the sample mean, the sample median, and a histogram. Estimation by the method of moments and minimum χ
2
estimation are explained, as are the concepts of bias, mean square error, and sufficiency.
Parametric Tests
Authors: Clark‐Carter David
Published: September 2014

Parametric tests derive their probability from probability distributions such as the normal distribution and the
t
distribution. They make more assumptions about the nature of the population from which samples have been drawn than their nonparametric equivalents. However, when the assumptions of a parametric test are met, the test will have more statistical power than a nonparametric equivalent.
Causality
Authors: Evandt Øystein
Published: September 2014

Different aspects of the causality concept are discussed, inclusive of the fact that no widely accepted definition of causality exists. Still the concept has been essential to the development of science. Further, aspects of how to establish causal relationships are discussed, including causality and statistics, empiricalversusmechanistic causal models, agreement of causal relationships within acceptable margins of error when statistical data are part of establishing such relationships, and Karl Popper's concept of falsifiability. A few concepts and tools related to causality in quality and reliability are briefly mentioned. Newer developments in causality and statistics are mentioned and references to important literature on these developments are given, in addition to references to more established knowledge.
Statistical Efficiency
Authors: Kenett Ron S.
Published: September 2014

The idea of adding a practical perspective to the mathematical definition of statistical efficiency is based on a suggestion by Churchill Eisenhart, who, in a 1978 informal “Beer and Statistics” seminar in Madison, Wisconsin, proposed a new definition of statistical efficiency. Later, Bruce Hoadley from Bell Laboratories, picked up where Eisenhart left off and added his version nicknamed “Vador”. Blan Godfrey, former CEO of the Juran Institute used this concept in his 1988 Youden Address on “Statistics, Quality, and the Bottom Line”, at the Fall Technical Conference of the American Society for Quality Control. Later, Kenett, Coleman and Stewardson in 2003 further expanded this idea adding an additional component, the value of the data actually collected. The concept of practical statistical efficiency (PSE) derived from these developments is introduced below.
Risk Analysis, Extremes in
Authors: Teugels Jozef L.
Published: September 2014

Over the last 25 years, extreme value theory has become a substantial part of statistical analysis. Often, extremes extract from data essential information that can hardly be retrieved from central tendency measures. We first deal with the main ingredients of classical extreme value theory, indicating its potentials and generalizations.
Cause‐and‐Effect Diagrams
Authors: Kenett Ron S.
Published: September 2014

Cause and effect is a basic knowledge driven by theoretical and empirical considerations. Several tools have been proposed to map cause and effect relationships, with some more heuristics some highly quantitative. In this section we cover the Ishikawa fishbone diagram, structural equation models, and Bayesian networks.
Factor Relationship Diagrams
Authors: Hild Cheryl , Sanders Doug
Published: September 2014

Design of Experiments consists of methodologies and data acquisition plans where specific product or process factors (X's) are manipulated across certain levels in order to understand their impact on critical performance parameters (Y's). However, experimental results are impacted by the choice of the design factors (X's), the design structure, and by decisions on how background and noise variables are managed during the experiment. The Factor Relationship Diagram (FRD) is presented as a visual tool effective in assisting in the planning of DOEs, the evaluation of alternative design plans, and in the interpretation of DOE results. The FRD also provides a visual means of describing the error structure and understanding the implications of restrictions on randomization in a DOE.
B
ayesian Networks in Reliability
Authors: Langseth Helge , Jensen Finn Verner
Published: September 2014

Over the last decade, Bayesian networks (BNs) have become a popular tool for modeling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. This article discusses the properties of the modeling framework that are of highest importance for reliability practitioners.
Data Mining in Quality and Reliability
Authors: Allen Elaine , Seaman Chris
Published: September 2014

Data mining techniques can be separated into two primary areas: those using supervised algorithms and those using unsupervised algorithms. Supervised data mining techniques most closely resemble standard statistical modeling because they are predictive, while unsupervised data mining techniques are associative and are used to examine relationships between variables and may or may not have an implied direction in the relationship. This article uses a large healthcare trial in which data mining techniques provided the only way to analyze the dataset for data quality.
Discrete‐Event Simulation for Reliability Prediction
Authors: Jones Jeff
Published: September 2014

Discrete‐event simulation is an empirical approach to system modeling that can allow us to build real‐world models and attempt to use them to predict what is likely to happen to the reliability of a system in the future. The underlying techniques used in this process involve random sampling from failure distributions, and representation of system structure using such techniques as Petri nets, Markov diagrams, and fault trees. These techniques allow us to build fairly realistic models of complex systems that can be used to understand their dependability behavior under various operating conditions.
Smoothed Function Estimation for Censored Data
Authors: Chaubey Yogendra P. , Sen Arusharka , Sen Pranab K.
Published: September 2014

We review the well‐known smoothing techniques for density and hazard function estimation under random censoring, developed in the recent past. We then outline how the recently proposed smoothing technique by Chaubey and Sen for uncensored data can be adapted to the case of random censoring.
Splines and other Metamodels in Reliability Analysis
Authors: Schueremans Luc , Gemert Dionys
Published: September 2014

Simulation‐based concepts are very attractive to be used in structural reliability because of their inherent simplicity. The major drawback, however, remains the large number of simulations required to obtain sufficiently accurate results. In case of large and complex structural systems with low failure probabilities, the processing time might become unrealistic. To cover this disadvantage, reliability analyses are based on simulation methods (Monte Carlo or directional sampling) in combination with an adaptive metamodel. Often use is made of a low‐order polynomial response surface. The use of splines, neural networks, or ordinary kriging may increase the overall performance.
Failure Modes and Effects Analysis
Authors: Snee Ronald D. , F. Rodebaugh William
Published: September 2014

This chapter gives an overview of failure modes and effects analysis (FMEA) and shows how it is allied in practice by discussing its use in improving manufacturing equipment performance. FMEA is an effective tool for the design of new products and processes and the improvement of existing processes. Using FMEA one can determine how a product, process, or system might fail and the likely effects of particular modes of failure. Having originated in the U.S. military in the 1940s, FMEA today is an analytical tool widely employed in approaches to quality and process improvement such as ISO 9000, ISO/TS 16949, Lean Six Sigma, and Design for Six Sigma.
Aging and Positive Dependence
Authors: Spizzichino Fabio
Published: September 2014

Multivariate survival models for vectors of dependent lifetimes typically manifest also a phenomena of aging. This chapter presents a discussion about the connections existing between these types of properties and point out some aspects that they have in common, from a conceptual point of view. For this purpose some basic notions of positive dependence and some notions of multivariate aging are being recalled.
System Signatures
Authors: Samaniego Francisco J.
Published: September 2014

The basics of structural reliability theory for engineered systems are briefly reviewed. Some of the literature on system signatures, defined in the context in which a system's components have independent and identically distributed (i.i.d.) lifetimes, is discussed. In brief, the signature of ann‐component system is a probability vector whoseith element is the probability that the ith component failure is fatal to the system. Representations are given for the survival function, density, and failure rate of systems with i.i.d. component lifetimes in terms of the system's signature vector and the common distribution of its components. The utility of signatures in the comparative study of system performance is illustrated through several “preservation” theorems. The occurrence of signatures in a variety of reliability contexts is noted. These include the study of closure theorems for the IFR class, comparisons between simple and indirect majority systems, system comparisons via stochastic precedence and via the information and the dispersive orderings, hazard rate ordering between independently drawn order statistics, optimization problems in reliability economics, and comparisons between communication networks.
B
ayesian Reliability Demonstration
Authors: Coolen Frank P.A. , Coolen‐Schrijner Pauline
Published: September 2014

This article presents several main aspects of Bayesian reliability demonstration, together with a concise discussion of key contributions to this topic.
Empirical
B
ayes Estimation of Reliability
Authors: Pensky Marianna
Published: September 2014

Empirical Bayes decision procedures are decision theoretic procedures that utilize past data as a means for bypassing the necessity of identifying a completely unknown prior distribution. The article reviews the parametric and nonparametric empirical Bayes techniques and their application to estimation of the mean lifetime, the reliability function, and the failure intensity function.
Degradation Processes
Authors: Lehmann Axel
Published: September 2014

Many failure mechanisms can be traced to an underlying process and stochastically changing covariates. The degradation process may represent wear, fatigue crack growth, corrosion, or material fatigue in engineering settings, and markers of health status and quality of life data in medical settings. Degradation data can provide much more reliability information than traditional failure‐time data and are particularly useful when the application of traditional reliability models is limited due to rare failures of highly reliable items or due to items operating in dynamic environments. A class of reliability models is considered in which failure is related to degradation due to the competing risks of hard and soft failure.
Optimal Reliability Design‐Modeling
Authors: Kuo Way , Wan Rui
Published: September 2014

Reliability has become a greater concern in recent years because high‐tech industrial processes with ever increasing levels of sophistication comprise most engineering systems today. To keep pace with this rapidly developing field, this chapter provides a broad overview of recent research on reliability optimization problems. In particular, we address issues related to:

UGF‐based optimal multistate system (MSS) design;

percentile life employed as a system performance measure;

multiobjective optimization of reliability systems, especially with uncertain component reliability estimations;

innovation and improvement in traditional reliability optimization problems, such as fault‐tolerance mechanism and cold‐standby redundancy involved system design.

The chapter concludes with a discussion of future challenges related to reliability optimization.
Mixture Models, Multivariate: Aging and Dependence
Authors: Spizzichino Fabio
Published: September 2014

Multivariate mixture models arise from certain situations of conditional independence. We discuss different aspects of these models. In particular, we point out that, under assumptions of stochastic monotonicity for the conditional distributions, they may present properties of positive dependence along with negative aging.
Coefficient of Determination (

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Authors: Bucchianico Alessandro Di
Published: September 2014

Coefficient of determination is a goodness‐of‐fit measure for models based on the proportion of explained variance. Variants of the coefficient of determination and pitfalls in the use of it are explained. The relation with the multiple correlation coefficient is explained.
Sample Size Determination in Hypothesis Testing and Estimation
Authors: Shore Haim
Published: September 2014

Sample data may be collected with different objectives in different scenarios. In some cases, one may wish to collect enough observations that would guarantee minimal prespecified reliability for the conclusions derived from the data. On other occasions, in addition to specified constraints regarding reliability, an optimal value is desired of some objective function that would guarantee that the sampling procedure is optimal in some sense. In all these cases, a major decision variable is the required sample size. In this article, sample‐size determination is addressed, relating, in particular, to four often‐encountered scenarios: hypothesis testing, parameter estimation, relational modeling, and optimal sampling.
Healthcare Performance Reports
Authors: Bevan Richard G.
Published: September 2014

This article is about the impacts of reporting healthcare performance within a country and confidential reports to individual clinicians as part of an audit process. It considers three approaches to reporting information: the “soft” approach, which is about quality improvement, focuses on success, and is voluntary; the “hard” approach, which is compulsory and about quality assurance; and the market approach, which relies on corporate or consumers' choices of health plans, and corporate or patients' choices of providers. It considers the impacts of reporting healthcare performance on four different groups: third party payers (either corporately or by consumers), patients, physicians, and providers. The evidence is that although publication of results has an impact, this is on providers and not
via
actions of patients or third party payers. The article suggests criteria that influence this impact and outlines statistical implications.
Statistical Issues in Vaccine Safety Evaluation
Authors: Farrington C. Paddy , Whitaker Heather J. , Hocine Mounia N.
Published: September 2014

This article describes some of the main statistical methods used to evaluate the safety of vaccines. The methods are illustrated using examples of public health concerns that arose following claims of associations, both real and unproven, between vaccination and adverse events. Prelicensure evaluation, surveillance methods, and observational study designs including case‐control, cohort, and case‐only methods are discussed.
Early Detection Programs in Medical Care
Authors: Davidov Ori , Hu Ping
Published: September 2014

Early detection programs are increasingly used to diagnose cancer and other diseases at an early stage. Evaluating their benefit is consequently of major interest. In this chapter we review the main ideas and methods used for assessing whether a screening program is beneficial.
Quality in Critical Care Medicine
Authors: Segal Eran
Published: September 2014

Critical care medicine is a growing element of modern hospital medicine. Critically ill patients consume about of 20% of hospital budgets and are therefore a very expensive part of hospital medicine. The occurrence of errors in ICU practice is determined by many factors including ICU design, workflow parameters, and the type of clinical procedures and patient population. Newer approaches to reducing error and improving expected outcome in the treatment of critically ill patients require monitoring and measurement of process, as well as of specific clinical outcomes such as the incidence of line related infections, ventilator associated pneumonia and also compliance with the tools needed to improve care. A great deal of research is directed at improving clinical care and outcome using measurements and improving treatment accordingly. Some information is available that ICUs which utilize these tools may provide better outcome.
Statistical Process Control in Clinical Medicine
Authors: Winkel Per , Zhang Nien Fan
Published: September 2014

A control chart may be used to characterize a clinical process and subsequently to monitor it. The chart depicts the values of a measurable quantity characterizing the process, e.g., the fraction of patients dying per month during a specified surgical procedure.

When the results of the process are stable the quality of the process may be assessed, e.g., by estimating the fraction of results that do not have an acceptable quality.

As an example, it is shown how the daily average of patient waiting times at an outpatient clinic is adjusted and subsequently monitored. If the variable one wants to monitor is an outcome measure, risk adjustment is necessary in order to adjust for variation between the patients in terms of severity of disease, comorbidities, age, sex, etc.

The outcome results of several healthcare providers (e.g., hospital departments) may be compared if the patients are assigned at random to the providers. Using simulation it is illustrated how an unknown or unmeasured risk factor may bias the comparison of the quality of care between two healthcare providers if observational data are used. It is recommended that risk‐adjusted comparisons based on observational data are considered inconclusive and used as inspiration for further investigations.