bluuebunny/crossref_metadata_2025 huggingface dataset:
Risk–Benefit Analysis for Environmental Applications
Authors: Gaylor David W.
Published: September 2014

The issue examined in a risk–benefit or risk–risk analysis is the trade‐off between losses suffered from illnesses or diseases, perhaps due to exposures to an environmental contaminant, and losses due to toxic effects induced by environmental remediation, e.g., chemicals used in cleanup or disinfectants employed to reduce microbial exposures. Risks and benefits may be incurred by different individuals. A management or societal issue arises as to whether the benefits (e.g., reductions of the severity or incidences of illnesses, diseases, or death) outweigh the risks introduced by interventions, e.g., disinfectant by‐products from chemicals used for drinking water treatment. A risk–benefit issue also arises for drugs with side effects and vitamins or essential minerals that become toxic at high doses. A procedure is described for estimating the optimum dose for a chemical intervention, drug, vitamin, or essential mineral that minimizes the total expected loss from the risk of an illness or disease and the loss induced by chemical, drug, or dietary intervention.
Group Decision
Authors: Morton Alec
Published: September 2014

The practice of risk assessment depends heavily on insights from the theory of the individual decision maker, both normative and behavioral. However, most risk related decisions impact on, and involve, multiple parties who may have interests which are not congruent. This article considers what light normative and behavioral theories of group decision can shed on risk assessment problems.

This article reviews ideas from game theory, such as the Nash equilibrium and the Nash bargaining solution, and relates them to a nuclear‐waste management example. Key ideas from the theory of social choice, such as Arrow's Paradox and Sen's Impossibility of a Paretian Liberal are also discussed. Alongside this normative theory, some of the key findings from the social psychological literature about how preferences are formed in groups are also described, focusing particularly on theories of social influence and phenomena such as polarization and groupthink.

The theories of group decision alert the analyst to ways in which group decisions can be badly made: they may be unfair, unsustainable, arbitrary, or based on poorly shared information, or deliberately or accidentally suppressed dissent. Fortunately, it is possible to design decision processes which make appropriate use of risk and decision analytic technologies, but which are also responsive to the social context in which the decision takes place.
Counterterrorism
Authors: Karr Alan F.
Published: September 2014

In today's world of complex politics and instant communication, terrorist attacks have assumed massive importance in terms of risk reality and risk perception. Strategies, mainly by national governments, to prevent, deter, mitigate, or otherwise respond to terrorist attacks are known ascounterterrorism.

This article provides an overview of counterterrorism drawn exclusively from public information. The problems and issues are placed in a context of risk and risk perception, but we argue that extant paradigms for risk analysis may not apply, for at least two reasons. First, most approaches will be overwhelmed by false positives, given the extreme rarity of events that have occurred and the (conceptual to methodological) difficulty of predicting events of unknown nature. Second, not much of the data is available for broad scientific analysis and verification. Public attitudes and perceptions are discussed throughout.
Considerations in Planning for Successful Risk Communication
Authors: Jardine Cynthia G.
Published: September 2014

Successful risk communication does not occur serendipitously—it is the result of careful planning and consideration of multiple needs and objectives. Some considerations for planning for successful risk communication are presented here. However, this list is neither fully comprehensive nor exhaustive. The best overall guidance is to try to view the risk through the eyes of those who may legitimately and rationally view the nature and consequences of the risk differently. Evoking an empathetic understanding of different perspectives of risk, together with the application of good judgment about the nature of the risk, is ultimately the key to initiating a productive dialogue on risks.
Stakeholder Participation in Risk Management Decision Making
Authors: Jardine Cynthia G.
Published: September 2014

Stakeholder participation in risk management processes is becoming increasingly recognized as a means of producing decisions that are responsive to varying interests and values, including those of the community. Some considerations for planning for successful stakeholder involvement are outlined. Goals against which stakeholder participation efforts can be evaluated are also provided. However, the “right” process of participation must be determined on the basis of the nature of the risk and the impact of the decision.
Dirichlet Process, Simulation of
Authors: Laud Purushottam W. , Pajewski Nicholas M.
Published: September 2014

We describe the Dirichlet process and its basic implementation in Bayesian hierarchical modeling. In particular, we discuss Dirichlet process mixture models with emphasis on computational strategies for both conjugate and nonconjugate forms of these models in the context of Markov chain Monte Carlo simulation.
Prior Distribution Elicitation
Authors: Quigley John , Bedford Tim , Walls Lesley
Published: September 2014

An overview of key issues associated with the elicitation of a prior probability distribution is provided. The nature of subjective probability is discussed, and the main causes of biased assessments are identified. Evidence is presented suggesting that people can improve their probability assessments under the correct conditions. The ways in which the elicitation of a prior distribution can be embedded within a process to minimize the impact of bias are examined. More advanced issues, namely validation of a prior distribution and reducing the cognitive burden on experts, are described.
Bootstrap and Jackknife, Overview
Authors: Martin Michael A. , Roberts Steven
Published: September 2014

A quarter of a century ago, the advent of fast, cheap computing saw the emergence of computationally intensive methods that sought to exploit the data itself as a resource for information rather than relying on restrictive classical distributional assumptions that often failed to hold in practice. Methods such as permutation procedures and the whimsically named jackknife and bootstrap have found wide use among practitioners in a variety of contexts. In this article, the basic uses of resampling techniques are described, with particular attention to bias and variance estimation, and confidence interval construction.
Data Mining, Evaluation Techniques in
Authors: Gür Ali Özden F.
Published: September 2014

In this article, we focus on evaluation techniques for data mining models after feature selection and parameter estimation have already been performed. Though performance evaluation techniques for data mining models are typically independent of the data mining task, goodness measures
are
task specific. We discuss the rationale, and the pros and cons of different goodness measures by data mining task, using illustrative examples to explain the concepts. We cover the following data mining tasks: classification, regression, rank prediction, clustering, and market basket analysis tasks. We provide detailed discussions for accuracy measures such as misclassification rate, confusion matrix, misclassification cost, quadratic and informational loss, ROC curve and lift chart, RMSE, MAD, AIC, BIC, Spearman's ρ, and Kendall's τ, within and between cluster distance. The following performance estimation techniques are discussed: resubstitution, holdout,
v
‐fold cross‐validation, leave‐one‐out/jackknife, and bootstrap. Further, we cover statistical hypothesis testing for comparing performance of two algorithms for the same data mining task. Also, references for further reading on the topics covered in this article are provided.
Classification and Regression Tree Methods
Authors: Loh Wei‐Yin
Published: September 2014

A classification or regression tree is a prediction model that can be represented as a decision tree. This article discusses the C4.5, CART, CRUISE, GUIDE, and QUEST methods in terms of their algorithms, features, properties, and performances.
Homogeneity of Variances
Authors: Koning Alex J.
Published: September 2014

The lack of homoscedasticity affects the validity of statistical tests, and has a negative effect on the coverage of confidence intervals and on the accuracy of estimators. Formal diagnostic tests and diagnostic plots are available for analysis of variance, the linear regression model and the nonparametric regression model. Box–Cox transformations are used to reduce the lack of homoscedasticity.
Measures of Location
Authors: Bucchianico Alessandro Di
Published: September 2014

Example of sample statistics that describe the center of gravity are given, together with some properties.
Measures of Scale
Authors: Bucchianico Alessandro Di
Published: September 2014

Example of sample statistics that describe spread are given, together with some properties.
Probability Density Functions
Authors: Everitt Brian S.
Published: September 2014

Probability density functions are the foundations of almost all statistical methodology. Here we give a brief account of the density functions most often encountered in practice along with a summary of their most important properties.
Heteroscedasticity
Authors: Morrison Donald F.
Published: September 2014

Homoscedasticity is the condition under which a series of observations have the same degree of variation. Heteroscedasticity is its negation. Homoscedasticity is assumed in the standard models for least‐squares linear regression, the two‐sample
t
test and the analysis of variance, and the effects of heteroscedasticity in these situations are discussed here.
Process Maps and Statistics
Authors: Leitnaker Mary , Cooper Tony
Published: September 2014

This article describes the use of the process map in process improvement activities. A definition of a process map is provided and the construction of a map is discussed and illustrated. The article also discusses how and why process map can be an invaluable tool for the effective use of statistically designed studies and experiments for improving process operation.
Fault Tree Analysis for Large Systems
Authors: Andrews John D.
Published: September 2014

This article describes the binary decision diagram (BDD) technique of fault tree analysis. The BDD provides an alternative to the traditional fault tree analysis method of kinetic tree theory which is progressed in two phases. The first phase is a qualitative stage to determine the minimal cut sets. The second stage then uses the minimal cut sets together with the component failure probabilities to conduct the system quantification. For large fault trees, this process is inefficient.

While fault trees express the system failure logic in a convenient way from the engineering viewpoint, they do not lend themselves to the mathematical manipulation. BDDs express the failure logic in a disjoint form, which makes quantification very fast. The calculations require just one pass through the BDD structure. Minimal cut sets are not required as an intermediate step, although there are methods by which they can be obtained if they are of interest. Calculations are exact and do not need any form of approximation. The trade‐off for the gains in speed and accuracy is that the fault tree has to be converted to a BDD. The advantages of this are more evident on large, complex fault tree structures.
Survival Analysis, Nonparametric
Authors: Kottas Athanasios
Published: September 2014

Survival analysis was one of the first areas where Bayesian nonparametric priors were utilized to enhance the flexibility of standard parametric models. Bayesian nonparametric methods are very well suited for survival data analysis, enabling flexible modeling and rich inference for the survival function or hazard function, providing techniques to handle censoring, and allowing incorporation of prior information. In this article, we provide a review of Bayesian nonparametric and semiparametric approaches, based on mixture models, to the analysis of univariate survival data. We discuss mixture models for survival distributions in a fully nonparametric setting as well as for error distributions in semiparametric accelerated failure time regression settings.
Physical Degradation Models
Authors: Wang Xiao
Published: September 2014

Many physical systems degrade over time. Recent advances in sensing and measurement technologies are making it feasible to collect extensive amounts of data on physical degradation. Degradation data are a rich source of reliability information and offer many advantages over time‐to‐failure data. We present some common physical degradation models and describe their applications for reliability inference.
Degradation and Failure
Authors: Nikulin Mikhail
Published: September 2014

Degradation and failure time data are usually used in reliability, bioreliability, biology, and medical statistics to estimate the survival or the intensity functions. The degradation data are often a rich source of reliability information and offer many advantages over failure time data, when the aging process due to cumulated wear and tiredness is observed. Some statistical approaches to model the relationship between degradation and failure data are considered here.
Competing Risks: Overview
Authors: Lindqvist Bo Henry
Published: September 2014

Consider a unit which can experience any one ofkcompeting failure types, and suppose that for each unit we observe the time to failure,T, and the type of failure,C ∈ {1, 2, …,k}. The case of observing the pair (T,C) is termedcompeting risksin statistical literature. After considering some examples we review basic notation and theory of competing risks. In particular we consider the latent failure time approach to competing risks in which thekrisks are represented by potential failure timesT1, …,Tkwhere only the smallest,T = minjTj, is observed together with its indexC = arg minjTj. In reliability studies, the marginal distributions of theTjare often of primary interest, but are unfortunately nonidentifiable in general. Additional, though nontestable, assumptions to obtain identifiability are considered, as are bounds for the marginal distributions given in terms of observable functions. The likelihood function of right censored competing risks data is given and its consequences for both parametric and nonparametric estimation are explained. Extensions of the classical theory of competing risks to more general Markov models and to repairable systems are briefly discussed.
Damage Processes
Authors: Singpurwalla Nozer D.
Published: September 2014

The point of view that we adopt here is that damage is an abstract notion that conveys an intuitive import. Specifically, an item fails when its damage exceeds a threshold. Whereas damage cannot be observed and measured, the surrogates that it spawns such as crack growth, CD4 cell counts, and wear, can be. These surrogates are known asmarkers. With the above in mind, we offer here a probabilistic architecture that endeavors to make the notion of damage precise and still retain its intuitive import. Our architecture looks at damage as a cumulative hazard and describes its evolutionviaa nondecreasing stochastic process. The observable markers associated with damage are also modeled by a stochastic process that is cross‐correlated with the damage process. Thus a bivariate stochastic process with one component that is nondecreasing and the other that may fluctuate around some mean could be a suitable model for encapsulating the phenomenon of damage and its markers. The latent nondecreasing process leads to the fluctuating observable processes, and an item fails when the former hits a threshold. The second feature of our architecture pertains to the threshold. We argue that the threshold needs to be random and has an exponential distribution with scale one; we call such a threshold thehazard potentialof an item. To conclude, our perspective on what constitutes a damage process is starkly different from that which is prevalent in the reliability and the survival analysis literatures. Hopefully, it offers a platform for describing an ill‐defined but much‐discussed phenomenon.
Software Reliability:Bayesian Analysis
Authors: Wiper Michael P.
Published: September 2014

In this article, the use of Bayesian methods in software reliability predictions is reviewed. In particular, the article studies software reliability models for interfailure times or for the numbers of failures in a given time period. After a general introduction to the use of the Bayesian approach to inference in the software reliability field, in particular, commenting on the use of the prequential likelihood for model checking, the article concentrates on reviewing Bayesian analyses of some of the most well known software reliability models, outlining the concatenated failure rate model of Jelinski and Moranda. The article ends with a real data example where the goodness of fit and reliability predictions of various models are compared.
B
ayesian Reliability Analysis
Authors: Martz Harry F.
Published: September 2014

Bayesian reliability methods permit the formal incorporation of pertinent supplementary information about the parameters of interest in a statistical analysis beyond that contained in the sample data. This additional information is contained in the prior distribution of the parameters. Bayes' theorem is used to combine the prior and sampling distributions to form the posterior distribution of the parameters. Then all the desired inferences are obtained from this joint posterior. In most practical applications, Markov chain Monte Carlo sampling techniques are used to numerically perform the required calculations. A real‐world reliability example that illustrates the Bayesian approach is presented.
Bayesian Design of Reliability Experiments
Authors: Meeker William Q. , Zhang Yao
Published: September 2014

This article describes Bayesian methods for planning reliability experiments. A Bayesian reliability experiment is a reliability experiment in which the experimenters intend to use prior information and for which Bayesian methods will be used in making inferences from the resulting data. Within the framework of a general model for reliability involving unknown parameters, we describe the basic ideas of Bayesian experimental design. We then describe a large‐sample normal approximation that provides an easy to interpret yet useful simplification to the problem of Bayesian evaluation of reliability experiments and discuss aspects of finding optimum experiments within this framework. We point to several examples of the methodology that are available in the statistical literature.
Maintenance andMarkov Decision Models
Authors: Dekker Rommert , Nicolai Robin P. , Kallenberg Lodewijk C.M.
Published: September 2014

In this chapter we first give an introduction to Markov decision theory. We state the main optimality criteria and solution approaches. Next we sketch how it can be applied in maintenance theory. In particular, we deal with the civil infrastructure sector and show what kind of results it brings. Finally we also indicate which problems arise in applications.
Masked Failure Data:Bayesian Modeling
Authors: Kuo Lynn
Published: September 2014

In the analysis for masked system lifetime data, the exact component that causes the system's failure is often unknown. For each series system at test, we observe its system's failure time and a set of components that includes the component actually causing the system to fail. The objective is to make inferences for the reliability of the components and their diagnostic probabilities. Basic Bayesian models are reviewed. Typically, both Expectation and Maximization (EM) and Markov Chain Monte Carlo (MCMC) algorithms are employed for inferences.

Both exponential distributions and Weibull distributions for the component lifetimes and some masking probability models are used here to illustrate these techniques.
Multivariate Stochastic Orders and Aging
Authors: Belzunce Félix , Shaked Moshe
Published: September 2014

Many aging properties of univariate random lifetimes can be characterized by various stochastic orders. As a result, several efforts have been made in the literature to define or characterize aging notions of random vectors of lifetimes by means of multivariate stochastic orders. Such definitions enable researchers to apply the intuition that comes with the univariate aging notions to complex systems. The characterizations are useful for the identification of the corresponding aging properties and for a better understanding of the meaning of these properties. In this article we describe such definitions and characterizations.
Scan Statistics in Quality and Reliability
Authors: Glaz Joseph
Published: September 2014

Scan statistics have been used extensively in several areas of science and technology to test for the occurrence of clusters of rare events. In this article we present a survey of results in the area of scan statistics that are applicable to quality control and reliability theory. In quality control, scan statistics have been used in the design and analysis of acceptance sampling schemes. We survey results that are useful in evaluating the operating characteristics of such sampling schemes. In reliability theory, scan statistics have been used in the analysis and design of a
k
‐within‐consecutive‐
m
‐out‐of‐
n
:
F
system of
n
components arranged in a linear or circular fashion. This system fails if
k
components fail within any segment of
m
consecutive components. In this article, we survey results for approximations and bounds for the system reliability of such systems. Two‐dimensional scan statistics have been used in the analysis and design of a
k
‐within‐consecutive‐(
m
1
,
m
2
)‐out‐of‐(
n
1
,
n
2
):
F
system of
n
1
n
2
components arranged in an
n
1
by
n
2
rectangular or cylindrical lattice. This system fails if
k
components fail within an
m
1
by
m
2
rectangular subregion in the
n
1
by
n
2
lattice. We survey results for the approximations and bounds for system reliability of these systems. In material science, reliability of certain materials can be compromised by the appearance of a large number of microcracks in a small area or volume. We present a survey of two‐ and three‐dimensional scan statistics that have been used in analyzing the occurrences of such microcracks.
Intercalibration Studies
Authors: Barry Jon , Allen Yvonne
Published: September 2014

The Biological Effects Quality Assurance in Monitoring Programmes (BEQUALM) scheme was initiated to develop quality standards for a range of biological effects techniques and to monitor compliance of laboratories generating data from these techniques for national and international monitoring programs (e.g. Oslo and Paris Commissions) and also for regulatory purposes. Each year, the lead laboratory (Cefas) organises a program of intercalibration exercises for each of the methods included in the scheme. In this paper, the results from a 2005/2006 intercalibration using a 48‐h acute test with
Daphnia magna
(water flea) are described.

Each intercalibration exercise is divided into two parts. In the first, laboratories were given an unspecified concentration of a reference chemical (zinc sulphate) which they were asked to dilute by a given factor and estimate the toxicity of the substance in terms of the percentage mortality. In the second part, a predetermined dilution series of the reference chemical was prepared by each laboratory from a provided stock and mortality at each concentration measured. From these mortalities, the medial lethal concentration (EC
50
) was estimated for each laboratory. We describe how these results are analyzed and, in particular, how compliance of laboratories is evaluated. The aim is to bring about continuous improvements in the methods adopted by the laboratories taking part.
Disease Surveillance
Authors: Lawson Andrew B.
Published: September 2014

This work focuses on disease surveillance and issues related to prospective detection of changes or aberrations in health data. One feature of recent interest in this area is the use of ancillary variables to allow for early detection of outbreaks. For example, gastro‐intestinal disease outbreaks can be detected by examining over‐the‐counter sales of pharmaceutical products. This work is sometimes known as syndromic surveillance. The focus of the detection will define the correct methods used in detection. For example, if a cluster of risk is to be detected then different methods would be used that if a jump in risk over time were to be detected.
Healthcare Policy and Management of Stroke, Breast Cancer, and Mental Health
Authors: H. Lakhani Azim D.
Published: September 2014

This brief article shows the kinds of health‐outcomes related questions that policy makers and managers ought to be asking about health care, such as levels of risk to health, levels of disease and ill health, potentially avoidable adverse health events, health‐related quality of life, and premature death. The focus is on what is achieved through resource use. The role of statistics in monitoring this, and the constraints in the availability and use of statistics have been illustrated using data on stroke, breast cancer, and mental health from the National Health Service in England. Trends in a variety of indicators such as obesity, treated and controlled high blood pressure, cancer incidence, cancer screening, hospital admissions, timely discharge, emergency readmission after discharge, and premature deaths show improvement over years but there is still room for further improvement. The data also show variation by geography, gender, and social deprivation. Aspects of monitoring not covered by routine data and ways of filling in gaps are described. Information on outcomes is one part of wider monitoring, covering the organization of services, resources available, access to and quality of service delivery, and patient perspectives, including their satisfaction with the services provided. This article is of necessity, an oversimplification of what are in reality extremely complex systems and issues.
Self‐Controlled Case Series Analysis
Authors: Hocine Mounia N. , Whitaker Heather J. , Farrington C. Paddy
Published: September 2014

The case series method was developed by Farrington 1 to investigate the strength of association between a time‐varying exposure and a potentially recurrent adverse event, using cases only. The method has been widely used in pharmacoepidemiology, particularly in the study of vaccine safety. The method is derived from an age‐dependent Poisson model by conditioning on the number of events and on exposure histories. The conditioning leads to estimation within individuals, and thus the effects of fixed covariates cancel out. In some circumstances, the case series method has high efficiency, relative to the cohort method. A semiparametric version of the case series method, in which the underlying age effect is left unspecified, is also available.
Patient Opinion Measures
Authors: Wensing Michel
Published: September 2014

This contribution aims to describe some statistical aspects of the application of patient opinion measures in healthcare. Examples of these applications are educational feedback to healthcare providers, use in formal accreditation procedures in care providers, and public reporting on patient opinions. While some patient opinion measures have been validated to some extent, statistical issues related to their application tend to be ignored. Three domains are discussed in this contribution: the sampling of study participants (procedures, response rates, statistical power), the handling of items (scale construction, missing values), and the presentation of findings (confounders, uncertainty). The Europep instrument is used throughout the paper for illustration of the points. This is a 23‐item questionnaire of patient evaluations of general practice, which has been widely used across Europe. Statistical aspects of applying patient opinion measures should be addressed to avoid bias, also if the measure itself was carefully developed and validated.
Bias in Modeling and Monitoring Health Outcomes
Authors: J. Grigg Olivia A.
Published: September 2014

The issue of bias is discussed in relation to the combined activities of both modelling health outcomes and monitoring them. As a motivating example, six‐monthly infection rates over the period April 2001 to September 2005 of MRSA in a UK NHS Trust are considered. Given a signal in the interval October 2003 to March 2004 of a potential increase in the underlying infection rate, it is described and discussed in detail how one might go about re‐estimating the rate of infection and perhaps adjusting it for bias.
Error Proofing, Healthcare
Authors: Nakajo Takeshi , Clapp Timothy G. , Godfrey A. Blanton
Published: September 2014

Many error proofing solutions are implemented successfully in healthcare. Most of them, however, are generated individually without the benefit of the knowledge of the wider body of error‐proofing solutions in related healthcare areas. Almost no healthcare organization has the benefit of the extensive experience in error proofing in manufacturing industries or other service industries. This article provides a simple methodology for applying error proofing principles and proven healthcare solutions for systematically generating workable solutions to reduce human error.
Cost of Quality
Authors: Godfrey A. Blanton , Moore Joseph D.
Published: September 2014

The understanding of costs of quality has evolved considerably over the past few decades. Traditionally, quality costs were thought of as prevention, appraisal, internal failures, and external failures. We also thought there was an optimal level of quality where costs were minimized. Newer thinking of quality costs has questioned the concept of optimum level, added the concept of quadratic loss, and explored many other costs associated with the costs of poor quality. Often, these new costs are far, far greater than the traditional costs.
Target Costing
Authors: Faltin Donna M.
Published: September 2014

Target costing is a proven, market‐driven strategy for product and service design. First conceived in the 1960s in Japan, target costing expanded the concept of value engineering to a customer‐focused, profit‐driven cost system that focuses on price.

Traditional “cost plus” reasoning sets the price of a product or service by starting with anticipated or realized costs, and adding the desired profit margin. This approach risks setting prices at a level the market will not support, with consequent risk of losing part or all of the capital invested in the introduction of a new product or service. Target costing, on the other hand, starts with the price that market conditions warrant, and factors in desired profitability to yield a “target cost” that must be met in the design and delivery of a product or service. By starting from market realities and explicitly accounting for business needs, target costing makes business success a design requirement, and helps avoid costly financial disasters in new product or service introduction.
Organizational Assessment Models
Authors: Conti Tito A.
Published: September 2014

The paper discusses the origin, development, and foreseeable trends in the area of organizational assessment models. The origin is traced back to the 1930s/1950s, when the problem of assuring quality in new product development became a competitive issue. “Quality assurance (QA) standards” emerged in the 1950s/1960s, together with methods to check compliance of the organization with such standards. Methods took the form of independent assessments, aimed at checking the capability of the product generating system (first‐party audits). The opportunity to assess supplier capabilities in advance soon led to the extension of the standards/assessment approach to B2B/B2A relations.
External
QA standards were then conceived, as the basis for assessing customer capabilities through “second‐party” audits. The evolution from proprietary external QA standards and second‐party audits to sector, national, and international standards and third‐party audits is described, with a particular emphasis on ISO 9000 standards and certification. Discontinuity in assessment models took place in the 1980s, with the outbreak of the new quality based competition and the advent of the so‐called total quality management (TQM) models. The paper examines the different types of TQM models in use today, from the quality award models to those focused on the optimization of performance measurement, and the “diagnostic models” for organizational improvement. Finally, trends in organizational models are discussed: first the evolution from organizational assessment to organizational improvement models; second the injection of more systems thinking into the models.
Policy Deployment for Performance Improvement
Authors: Watson Gregory H.
Published: September 2014

Policy deployment is a Japanese‐originated management process that applies the Deming cycle of plan‐do‐check‐act (PDCA) to the processes of planning and execution. Policy deployment has sometimes been called
management by objectives
(MBO)
on steroids
or
Turbo‐MBO
. A policy deployment process coordinates the definition and implementation of strategically important business‐process‐change projects to improve the routine operating processes of an organization. Policy deployment aligns and coordinates the activities of the organization through a number of mechanisms that open lines of communication across the organization in cross‐functional and cross‐layer dialogs. These communication mechanisms solicit ideas for making business improvements, define projects and objectives through an interactive “catchball process”, and review progress through cross‐layer improvement project assessments. This in‐depth involvement of the entire organization in both the planning and execution processes creates acceptance for change through the active participation of an engaged workforce. Synonyms for policy deployment include
hoshin kanri
(the original Japanese name) as well as policy management and management by policy (MBP).
Quality Control, Computing in
Authors: Noguera John
Published: September 2014

The article provides a brief survey of important advances in quality control tools that are (or should be) available to practitioners using current statistical quality software tools. The tools discussed include the use of the noncentral distribution function for power and sample size, confidence intervals in measurement systems analysis, dealing with nonnormality and autocorrelation in statistical process control, and the use of genetic algorithms in the design and analysis of experiments.
Neural Networks in Statistical Process Control
Authors: Hwarng H. Brian
Published: September 2014

The tremendous interest and wide applicability of neural computing theories and technology motivate researchers to explore how this class of modeling tools can be effectively applied to statistical process control (SPC). The basic principle of SPC is to signal an out‐of‐control state when the behavior of critical quality variables in a process is considered statistically abnormal, but also to leave the process intact when no evidence of statistical abnormality is present. Two primary categories of problems are the detection of shifts and the recognition of unnatural patterns from the process monitored. The article focuses on models appropriate in the SPC context and the general modeling approach employing various neural network algorithms. We illustrate the use of the approach with data generated from a bivariate autocorrelated process. While the neural network approach is flexible, adaptable, and capable of learning, it is limited by a lack of analytical robustness and the need for intensive data. Nevertheless, the approach enhances process‐troubleshooting capabilities and serves as a powerful alternative to the traditional SPC approaches in handling data collected from a dynamic and complex environment.
Monitoring of Safety in
EU
Railways
Authors: Pira Angelo , Piazza Roberto , Rajan Jane
Published: September 2014

According to the legislative framework of the European Union (EU) in the field of railway safety, information on safety performance is mainly collected to provide input to the development of an objective tool for setting common safety targets (CSTs) and assessing their achievement. CSTs will be developed in subsequent sets, on the basis of safety performances in member states (MSs) and their possible economic impact in terms of costs and benefits. European Railway Agency (ERA) is in charge of these projects, which are carried out in conjunction with industry and MSs in working groups of experts. The flow of information and data exchange on safety issues between the concerned parties is completed by the activity of accident investigation, carried out by National Investigation Bodies and communicated to ERA; this aims to better understand the causes of accidents, which, together with statistics on accidents, precursors to accidents, and economic aspects of safety, provides the necessary information to ERA for setting CSTs at EU level.
Statistical Quality Control
Authors: Parr William C.
Published: September 2014

This article discusses statistical quality control. Statistical quality control dates from the 1920s. Shewhart effectively launched the field of statistical quality control with a memorandum issued on May 16, 1924 featuring the first control chart. These ideas and others have been developed over many years to provide a reasonably complete and versatile set of statistical tools for studying, monitoring and improving processes using a wide variety of statistical methods.
Quality Function Deployment
Authors: El‐Haik Basem
Published: September 2014

Quality Function Deployment (QFD) is a structured approach to defining customer needs or requirements and translating them into specific plans to produce products to meet those needs. The “voice of the customer” is the term commonly used to describe these stated and unstated customer needs, delights and wants. This understanding of the customer needs is then summarized in a planning matrix called house of quality (HOQ). These matrices are used to translate higher level “whats” or needs into lower level “hows”—functional requirements, design parameters and process variables over four phases to satisfy the voice of the customer (VOC). QFD is methodology that broadens or extends quality responsibility throughout a product, service or process development.
Control Charts for the Standard Deviation
Authors: Mitra Amitava
Published: September 2014

Control charts are graphical tools to monitor the activity of a process. They are used to determine if the variation in a process is due to the presence of common causes (those that are inherent to the process) or special causes (those that are not part of the process as designed). A process is said to be in statistical control when it is under a system of common causes. A standard deviation control chart monitors process variation, as measured by the standard deviation of observations, through subgroups collected over a period of time. Typically, three lines are depicted on a control chart—the centerline (CL), the upper control limit (UCL), and the lower control limit (LCL). Rules for determining out‐of‐control conditions are defined, the most commonly used one corresponds to a point falling outside the control limits. Some parameters for the construction of the standard deviation control chart are the subgroup size, frequency of sampling, and placement of the control limits from the CL, based on the choice of an acceptable level of a false alarm rate.
Autocorrelated Data
Authors: Wardell Don G.
Published: September 2014

Traditional statistical process control methods assume that consecutive observations on a control chart are statistically independent. Often observations from actual processes are serially correlated. In such cases, the use of traditional control charts often leads to misleading conclusions as to whether or not the process is under control. We illustrate the problems of using traditional control charts and review several methods that have been proposed to handle the autocorrelation. Some of the methods are model based while others are model free. The majority of the methods are improvements over traditional control charts, but none is able to completely overcome the inherent difficulty of monitoring autocorrelated data. Hence there are some cases where traditional charts, which are simpler to implement, should be considered even when the process is autocorrelated.
Profile Monitoring
Authors: Woodall William H.
Published: September 2014

In many practical situations the quality of a process or product can be characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is used to understand and check the stability of this relationship over time. At each sampling stage one observes a collection of data points that can be represented by a curve (or profile). In some calibration applications, the profile can be represented adequately by a simple straight‐line model, while in other applications more complicated models are needed. In this article, we discuss some of the general issues involved in using control charts to monitor such process and product quality profiles.
Control Charts, Selection of
Authors: Adams Benjamin M.
Published: September 2014

The control chart is an essential tool in the area of statistical process monitoring. As the applications of control charts become more varied, control charts are continuously being adapted to meet the new data environments. Many types of control charts and monitoring schemes are now available to practitioners. This article provides general guidelines for selecting appropriate control charts for a given application.
Applications of Extreme Statistics in Science and Engineering
Authors: Teugels Jozef L.
Published: September 2014

We offer a large number of typical applications of extremal thinking to risk in the widest sense of the word. A comprehensive list of references is included.
Proficiency Test Data, Population Characteristics of
Authors: Wells David E. , Cofino Wim P.
Published: September 2014

Data from the measurement of silicate, acenaphthylene, and DDT in the marine environment are used to examine the Cofino model. This is a model for the inference of population characteristics from experimental data using uncertainties. It uses mathematical principles taken from quantum mechanics and provides an alternative to robust statistics for obtaining the population characteristics of proficiency test (PT) data. The Cofino model identifies clusters of values within a dataset that exhibit a high level of agreement and for each cluster calculates the mean, variance, and the percentage of the whole dataset. The model uses all data, including left‐censored values, and can cope with bi‐ or multimodal distributions, skewed, or tailing data. The model is based on a normal distribution when applied to PT data, but the principles it uses can be applied to other distributions and data sources.
Blocking
Authors: Gilmour Steven G.
Published: September 2014

When an experiment is designed,blockinginvolves grouping the runs together in sets which are expected to give similar responses. It is used to improve the estimation of the treatment effects of interest. Complete blocks can be used if the number of runs in each block is equal to the number of treatments. Otherwise incomplete blocks are needed. Different methods of choosing incomplete blocks are used depending on whether the treatments are simple, discrete categories or combinations of levels of several factors. Extensions include resolved block designs and row–column designs, such as Latin squares.
Fractional Factorial Designs, issues in
Authors: Voelkel Joseph G.
Published: September 2014

If only a fraction of all the treatment combinations of a full factorial design are selected to be run, the resulting design is called a
fractional factorial design
. By carefully deciding which fraction of the treatment combinations to run, it is possible to construct fractional factorial designs with good properties. We examine these designs in detail for designs in which each factor is studied at two levels. We show how to construct these designs and how to find their properties. We discuss some uses of these designs as well as their advantages and disadvantages. An example is given of a design and its analysis. Extensions are considered to other situations, including three‐level designs, blocked designs, and split‐plot designs.
Half‐normal plot ‐ Some examples
Authors: Taam Winson
Published: September 2014

A half‐normal plot is a graphical tool to discern active contributors from inert ones in a factorial experiment. It is designed for two‐level factorial experiments but can be adapted for experiments with other levels. Plotting absolute effect estimates and their corresponding half‐normal scores provides a graphical display that compares the relative strength of various effects in the experiment. Its ease of use, visual appeal, and simplicity are reasons for its popularity.
Interactions with examples
Authors: Reese C. Shane
Published: September 2014

Statistical approaches for assessing the effect of a set of explanatory variables on a response variable often involve estimation of joint or interaction effects. This article provides a historical perspective on interaction effects from both an effects model and a cell means model perspective. Factorial designs are presented as an effective means of conducting studies where interaction effects are estimable. The assessment of presence or absence of interaction effects is demonstrated with both statistical hypothesis testing and graphical techniques. An example from the commercial automotive industry provides an illustration of interaction effects.
Main Effects
Authors: Beder Jay H.
Published: September 2014

This article defines main effects in a factorial experiment and illustrates the concept with several examples. The definition is stated in terms of contrasts in cell means. This article describes a common alternate approach using a “factor effects” parametrization, and explains the relationship between the two approaches. Finally, the article briefly describes how main effects are defined in a fractional factorial experiment.
Main Effect Designs
Authors: Jacroux Mike
Published: September 2014

In this article we consider experimental situations where a screening experiment is to be run and primary interest is in the identification of variables which have an influential effect on the process being studied. In such cases, main effects designs are often used. Main effects designs typically allow for the efficient estimation of main effects parameters and are particularly useful when the initial number of experimental variables being studied is large compared to the number of runs in the screening experiment. Here we describe a number of the orthogonal and nonorthogonal main effects designs available to practitioners along with some of their individual advantages and disadvantages.
Projectivity in Experimental Designs
Authors: Tyssedal John
Published: September 2014

Projectivity concerns the properties of a factorial design when it is restricted to a subset of experimental factors. In this article we stress the importance of taking projective properties into account when a screening experiment is performed. We present results that can be used to decide on the projectivity of two‐level designs, show by an example how these properties can be taken advantage of in order to identify active subspaces of factors and give various techniques that can be used to construct designs with good projective properties for various experimental situations.