bluuebunny/crossref_metadata_2025 huggingface dataset:
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Destructive Testing
Authors: Trip Albert , De Mast Jeroen
Published: February 2008

The fundamental problem of gauge capability studies is that an object can be measured only once. After this measurement the object is different, sometimes emphatically so because the measurement is destructive. A standard gauge repeatability and reproducibility (R&R) study, to determine the precision of the measurement, is designed for nondestructive measurements, and assuming temporal stability of the objects. If temporal stability is beyond assumption, or if measurements can only be obtained by damaging the object—both types are called
destructive measurements
—then additional assumptions are needed to estimate the measurement precision. Several possibilities are given and reviewed. The assumptions are essentially attempts to model object‐to‐object variation, aiming at separating this from measurement precision. The failure of these attempts will show up in larger than necessary estimates of the measurement variation.
Intercalibration Studies
Authors: Barry Jon , Allen Yvonne
Published: February 2008

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.
Healthcare Policy and Management of Stroke, Breast Cancer, and Mental Health
Authors: Lakhani Azim D. H.
Published: February 2008

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.
Statistical Issues in Vaccine Safety Evaluation
Authors: Farrington C. Paddy , Whitaker Heather J. , Hocine Mounia N.
Published: February 2008

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.
Case–Control Studies
Authors: Davidov Ori , Herman Amir
Published: February 2008

Case–control studies are among the most powerful and widely used study designs in epidemiology and medicine. They are used to investigate the influence of risk factors on the occurrence of disease. Case–control studies are conducted by sampling a group of cases and a group of controls and retrospectively comparing them with respect to an exposure. Our aim here is to provide some details on the conduct, sampling, analysis methods, variations, and limitations of the case–control design.
Patient Opinion Measures
Authors: Wensing Michel
Published: February 2008

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.
Clinical Behavior Change
Authors: van der Weijden Trudy , Wensing Michel
Published: February 2008

Many patients do not receive optimal care. Clinical behavior change is directed at improvements in patient care. A systematic approach to clinical behavior change is promoted. Research on clinical behavior change is the scientific study of methods to promote the systematic uptake of clinical research findings into routine practice. Both quantitative and qualitative approaches in data collection can contribute to the development and evaluation of interventions targeted at clinical behavior change. This chapter focuses on the collection and analysis of quantitative data to answer questions such as “What is current practice?” and “What is the effect of an implementation strategy?” There are several specific statistical issues in this research area, such as the validity of measurements of clinical behavior, interdoctor variation, and clustering effects affecting power calculation and level of analysis. These issues are discussed in this chapter, and illustrated with examples from a study describing the changing test‐ordering behavior of general practitioners.
Population Characteristics of Proficiency Test Data
Authors: Wells David E. , Cofino Wim P.
Published: February 2008

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.
Multiple Modes in Proficiency Test Data
Authors: Wells David E. , Cofino Wim P.
Published: February 2008

Chromium, chlorobiphenyls, and aluminum in marine matrices are some of the chemicals that, when measured by different laboratories, give rise to bimodal datasets. Bimodality in proficiency test (PT) data often results from the use of different chemical analytical methods or by contamination. The Cofino model provides an alternative approach to robust statistics using Huber (Hu) and Hampel (Ha) estimators for the evaluation of PT data. This is a model for the inference of population characteristics from experimental data using uncertainties. The Cofino model can be used to identify each mode in the distribution and provide the population characteristics of each mode without separating the data. Features affecting the breakpoints for the separation of the modes identified by the normal distribution approximation model and its application to the evaluation of PTs with bimodal distributions are discussed. In each case, the Cofino model provides a preferred consensus value compared with other robust statistical methods.
Coherent Systems
Authors: Neath Andrew A.
Published: February 2008

A system is coherent when each of its working components is beneficial to the system. In this paper, the reader is introduced to some properties of coherent systems.
Path Sets and Cut Sets in System Reliability Modeling
Authors: Tortorella Michael
Published: February 2008

This article reviews the concepts of connectedness, path sets, and cut sets in the context of analyzing a system reliability block diagram described as a labeled random graph. The methods discussed also lend themselves to the development of bounds for system reliability. Directions for generalization to capacitated networks are given.
Modules and Modular Decomposition
Authors: Zuo Ming J.
Published: February 2008

Modules are system structures that have well‐established reliability evaluation algorithms. The technique of modular decomposition identifies such modules in complex systems and uses their efficient reliability evaluation algorithms for reliability evaluation of the whole system. In this article, we provide the formal definitions of modules and modular decomposition for system reliability analysis, illustrate the technique of modular decomposition, and outline reliability evaluation algorithms for several well‐studied modules. References for further reading in this area will also be provided.
Repairable Systems Reliability
Authors: Ascher Harold E.
Published: February 2008

Almost all systems of interest in reliability applications are designed to be repaired, rather than discarded, after they fail. This article begins with a very brief introduction to probabilistic modeling and statistical analysis of repairable systems, bolstered by many cross‐references and other references. There are several superficially striking similarities between reliability models for repairable systems and for nonrepairable items. These similarities should have led to great emphasis on the up‐to‐infinite differences in model interpretations. The article's stress, therefore, is on the consequences of the almost universal and chronic overemphasis on relatively unimportant similarities between concepts and models for nonrepairable items
versus
those for repairable systems. Unfortunately, most reliability books and papers continue to emphasize these avoidable ambiguities, which exacerbate the unavoidable confusion caused by inherent ambiguities. The ensuing vicious circle virtually ensures errors, especially when performing failure data analyses. Some of the mistakes in reliability books and papers are so blatant that they are obvious even from “eyeball analysis”!
Stress–Strength Model
Authors: Basu Asit P.
Published: February 2008

In this paper, we consider estimation of reliability for stress‐strength models. Nonparametric, parametric, and Bayesian inferences are considered. The problems related to the exponential distribution are given in detail.
Repairable Systems: Statistical Inference
Authors: Agustin Ma. Zenia N.
Published: February 2008

This article discusses statistical inference procedures for a general class of models for repairable systems. In particular, estimation procedures as well as goodness‐of‐fit testing for an age‐dependent minimal repair model that incorporates covariates are presented. Examples involving real datasets are used to illustrate the applicability of the procedures.
Block Replacement
Authors: Dekker Rommert
Published: February 2008

Block replacement is the replacement of a block of components at fixed time intervals. Its aim is to prevent failures, which can have severe negative consequences, both with respect to economics and safety. It is appropriate if components exhibit an increasing failure rate. A main problem is to determine the optimal replacement time. For this purpose the block‐replacement model has been formulated. We discuss how the optimum replacement time can be computed and we compare block replacement with some other policies.
Imperfect Repair, Counting Processes
Authors: Rigdon Steven E.
Published: February 2008

Models for repairable systems are often categorized by the effect of a failure/repair on the subsequent performance of the system. This article looks at various assumptions regarding this effect. Models for minimal repair, renewal, imperfect repair, and various others are included.
Accelerated Life Tests: Classical Methods for Design and Analysis
Authors: Giorgio Massimiliano
Published: February 2008

Accelerated life tests are used to quickly obtain information about the failure time distribution of very reliable technological products. Tests are conducted in conditions more severe than the normal operating ones to accelerate the mechanism of failure and compress the life of components and materials of interest.

An estimate of the lifetime distribution at normal use conditions is extrapolated from data, obtained at higher stresses, through the use of appropriate statistical models that incorporate a special relationship accounting for the effect of stress conditions on specific lifetime characteristics of the products under study.

After a brief introduction that describes motivations and objectives of accelerated testing, the following topics are addressed:

methods for accelerating reliability tests;

models for classical accelerated tests;

procedures for point estimation, interval estimation, and checking model assumptions; and

basic criteria for planning accelerated life tests.

In this article only the classical approach to design and analysis of accelerated constant‐stress tests will be discussed. Moreover only fully parametric acceleration models are considered.
Accelerated Life Tests:Bayesian Design
Authors: Soyer Refik
Published: February 2008

In this article, the Bayesian decision theoretic setup for design of accelerated life tests (ALT) is presented. Some of the key contributions to the Bayesian design of ALT are reviewed. While doing so, approximate Bayesian designs based on linear Bayesian methods and Monte Carlo–based methods are discussed. Computational issues in the evaluation of expectation and optimization steps in the solution of the decision problem are considered and some recent Monte Carlo approaches that can reduce the computational effort in the design problem are discussed.
Accelerated Life Tests: Analysis with Competing Failure Modes
Authors: Bunea Cornel , Mazzuchi Thomas A.
Published: February 2008

Most of the accelerated life tests (ALT) ignores the possibility of competing modes of failure. The literature that attempts to address this problem often does so by assuming independence among the competing failure modes. Rather, the failure modes display a highly dependent structure, which is usually influenced by the applied stress. The dependent ALT‐competing risks models available are based on multivariate distributions with specified marginals. However, multivariate exponential and Weibull distributions—dominant in literature—have properties not suited for all applications. This article investigates the applicability of existent independent and dependent models on a couple of datasets, and presents a new ALT model with dependent competing failure modes. The dependence structure is modeled by a copula given the rank correlation. The effects of the dependence degree of the competing failure modes are also studied.
Software Failure Data Analysis
Authors: Kenett Ron S.
Published: February 2008

Effectively and efficiently analyzing software failure data is critical to competitive organizations developing software. This article covers several methods used to analyze such data, with examples. The methods covered include software reliability models for predicting the number of hidden faults, software trouble assessment for assessing the inspection process, Pareto charts of software errors, and control charts for tracking the weekly number of new and fixed bugs.
Life Testing from a
B
ayesian Perspective
Authors: Pulcini Gianpaolo
Published: February 2008

Life testing provides to the Bayesian analyst new information, which may lead to the revision of a belief held prior to observing the experimental data (
learning from experience
). The revised belief, expressed in terms of a posterior probability, constitutes the inferential result. Clearly, in a decision‐making approach, revising belief leads the expert to confirm or change the choice that would have been made among alternative courses of action on the basis of prior belief only. The way in which the observed data modify the current belief is formalized by Bayes theorem. As newer data is observed, today's posterior information becomes tomorrow's prior information, so that each time new observed data come in the current belief can be revised.

Clearly, the effect of life testing in updating expert belief depends on the size of the observed sample and on the manner in which the data have been collected, i.e. on the
design of life testing
. In a Bayesian perspective the choice of life testing design is made by optimizing the design with respect to a given overall loss function, which is the sum of a
decision loss
, associated with the need of making a decision under uncertainty, and a
sampling cost
, representing the cost of collecting life testing data.

Simple examples illustrate the Bayesian learning process and design optimization.
Cox Proportional Hazard Model
Authors: Cooke Roger M.
Published: February 2008

The sections in this article are

Introduction

Proportional Hazard Model

Model Adequacy

Censoring and Competing Risk

Conclusions
Critical‐to‐Quality Matrices
Authors: Hoerl Roger W.
Published: February 2008

In many applications, improvement teams need to take fuzzy, intangible customer needs, and translate them into measurable metrics. For example, the customer of an energy company producing electric generators may state that they want to deal with knowledgeable sales representatives from the company. This is a reasonable request, but how is the energy company to know if its sales representatives are “knowledgeable”? What does knowledgeable really mean, and how could the company measure it? Critical‐to‐quality (CTQ) matrices are one method used to take such fuzzy, intangible customer needs, and translate them into hard metrics that can be quantified. These metrics are often referred to as
critical‐to‐quality measures
. In addition to producing tangible metrics, these matrices also provide a prioritization of the metrics that enables focus on those metrics most critical to satisfying customers' needs. Such matrices can also be used in subsequent analysis, beyond identifying and prioritizing CTQs. For example, subsequent matrices can begin with CTQs and prioritize the most critical steps in the process that need improvement in order to impact the CTQs.
Concept Selection Matrices
Authors: Maass Eric Charles
Published: February 2008

Concept selection matrices provide a way to evaluate and select among several alternative approaches, architectures, or solutions using a set of selection criteria. The selected alternative might be one of the alternatives considered or a new alternative generated or developed as a hybrid of several alternatives during the concept selection process (referred to as
controlled convergence
). The concept selection matrix and associated process of controlled convergence is illustrated with a nontechnical and a technical example. Weighted and categorized matrices are also briefly discussed.
Team Building
Authors: O'Brien Margaret G.
Published: February 2008

Teams are not new to our workplace; they have been around for a very long time and have been used effectively for short‐ and long‐term implementation strategies in organizations, as evidenced by all the articles and books written on teams and team work over time. Interestingly enough, when we began to examine critical performance factors on technical projects, we discovered the following.

Improving: technical skills were not enough to sustain gains in an organization. One of the missing factors was a systematic application to team building to complement the scientific technical model used in Six Sigma at NC State University.

The perception of how to operate in and on a team was disparate across an organization. Everyone had a different concept.

Including modules on teams and team building as well as others on project management, and leadership and change management, in teaching a technical topic such as Six Sigma for technical professionals would probably enhance the positive outcomes for the participants.

The Six Sigma curriculum committee determined that we would provide multiple modules in every technical class taught at our university. The modules focused on basic skills including team roles, conflict and self‐assessment, and reward/recognition.

This article shares some of the results and findings from the implementation of team modules in the technical non credit Six Sigma curriculum with our business and industry noncredit classes at NC State University in Raleigh, North Carolina.
Multi‐Vari Charts
Authors: Shainin Richard D.
Published: February 2008

This article introduces multi‐vari charts. It shows how they can be used to break process variation into several components of variation each associated with a source of variation. Breaking process variation into these components enables one, using the Pareto principle, to identify the biggest component of variation. Eliminating the source of this component is usually the best way to reduce process variation. This paper also presents several interesting examples of the application of multi‐vari charts.
Lean Methods, Creating Flow with
Authors: Srinivasan Mandyam M.
Published: February 2008

Lean thinking and lean manufacturing are terms associated with effective delivery of goods and services to the customer. These are management tools that are typically used in conjunction with Six Sigma. This article presents a brief discussion on the real purpose of lean, which is to create a smooth flow of goods or services across the entire supply chain. It describes how lead‐time reduction is vital to achieve this purpose. Lean is an ongoing effort, and this article also briefly discusses how organizations can use the tools and techniques of lean to undertake the lean journey.
Error Proofing, Healthcare
Authors: Nakajo Takeshi , Clapp Timothy G. , Godfrey A. Blanton
Published: February 2008

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: February 2008

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.
Failure Modes and Effects Analysis
Authors: Snee Ronald D. , Rodebaugh William F.
Published: February 2008

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.
Quality Function Deployment
Authors: El‐Haik Basem
Published: February 2008

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.
Process Maps and Statistics
Authors: Leitnaker Mary , Cooper Tony
Published: February 2008

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.
Benchmarking in Project Definition
Authors: Watson Gregory H.
Published: February 2008

Benchmarking is a process of comparing performance between two or more organizations about a product, process or service that they have in common. The benchmarking objective is to identify a process that has superior performance and to determine the reasons behind that exceptional performance. Benchmarking studies may be conducted at either the strategic, or operational organizational level and they may compare competitive organizations, internal divisions or processes, industry‐wide practices, or generic business practices in cross‐industry studies. Benchmarking follows a four‐step process: plan‐collect‐analyze‐improve. In the planning step the objective of the study is defined and a literature search is made to determine what information is available from the public domain. Companies are targeted as partners in the study and a preliminary questionnaire is developed in order to solicit their participation. In the second step of the study surveys are conducted, site visits are accomplished, and interviews are used as part of the data collection process. In the analyze step, statistical and practical comparisons are made and best practices are identified among the participating organizations. In the final step, potential enablers of improvement are culturally adapted to each organization and they are implemented into the daily management system.
Total Quality Management (
TQM
)
Authors: Stephens Ken
Published: February 2008

The philosophies of the quality discipline have been and are being established over a large period of our recent history by many contributors. How the profession defines “quality” has much to contribute to its fundamental philosophies. A comprehensive definition, suitable to the broad range of quality's involvement, is used in this paper to expose many of its philosophies.
Theory of Constraints
Authors: Mordoch Avraham
Published: February 2008

The
theory of constraints
(
TOC
) is a holistic, overall management philosophy that aims to (a) resolve systems‐oriented problems and (b) continually achieve more of the goal of a defined system. If that system is, e.g., a for‐profit business, then the goal becomes one of making more money, in the present as well as in the future.
Target Costing
Authors: Faltin Donna M.
Published: February 2008

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: February 2008

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.
Multivariate Stochastic Orders and Aging
Authors: Belzunce Félix , Shaked Moshe
Published: February 2008

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.
Causality
Authors: Evandt Øystein
Published: February 2008

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.
Quadrature and Numerical Integration
Authors: Ausín M. Concepción
Published: February 2008

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.
Computational Issues in Network Reliability
Authors: Colbourn Charles J. , Shier Douglas R.
Published: February 2008

Systems of interconnected components can often be usefully represented by networks. It is practically important to calculate the reliability of such network systems when the components are subject to (statistically independent) failures. We define several commonly occurring reliability models and summarize the computational complexity of calculating their associated reliability measures. Some general techniques for exact and approximate calculation of network reliability are surveyed.
Data Mining in Quality and Reliability
Authors: Allen Elaine , Seaman Chris
Published: February 2008

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: February 2008

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.
B
ayesian Robustness: Theory and Computation
Authors: Betrò Bruno
Published: February 2008

The problem of analyzing the robustness of Bayesian procedures has been considered to some extent in the last few years. In spite of this, robustness analysis has not yet become part of routine Bayesian analysis mainly because of the inadequate development of numerical algorithms and related software. When robustness with respect to the prior is considered and the prior belongs to a class defined in terms of the so‐called generalized moment conditions, then the problem reduces to one of linear semi‐infinite programming (LSIP). The first part of this article is devoted to a review of the general theory of optimization of linear functions over generalized moment classes of measures. In the second part, its application to robust Bayesian analysis is considered, giving sufficient conditions for the existence and the identification of the required extremum values. Algorithms which have been proposed for actual computations are finally reviewed in the third part.
Survival Analysis, Nonparametric
Authors: Kottas Athanasios
Published: February 2008

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.
Asymptotic Reliability Analysis of Very Large Structural Systems
Authors: Adhikari Sondipon
Published: February 2008

In the reliability analysis of safety‐critical complex engineering structures, a very large number of the system parameters can be considered to be random variables. The difficulty in computing the failure probability using the classical first and second‐order reliability methods (FORM and SORM) increases rapidly with the number of variables or “dimension”. On the basis of the asymptotic distribution of quadratic form in Gaussian random variables, two formulations, for the case when the number of random variables
n
 → ∞, are provided. The first is called
strict asymptotic formulation
and the second is called
weak asymptotic formulation
. Both approximations result in simple closed‐form expressions for the probability of failure of an engineering structure. The proposed asymptotic approximations are compared with existing approximations and Monte Carlo simulations using numerical examples.
Perfect Sampling
Authors: Connor Stephen B.
Published: February 2008

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: February 2008

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.
Smoothed Function Estimation for Censored Data
Authors: Chaubey Yogendra P. , Sen Arusharka , Sen Pranab K.
Published: February 2008

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.
Dynamic Programming Methods in Repair and Replacement
Authors: Bhattacharjee Manish C.
Published: February 2008

Applications of dynamic programming to problems in reliability, with a specific emphasis on repair and replacement are surveyed. A brief summary of early classic work on such uses of dynamic programming is followed by a review of later approaches adopted by different researchers toward the problem of modeling plausible and pragmatic notions of imperfect repairs. We then provide a synthesis of how such different approaches for the study of repairable systems can be subsumed within a unified setting of a stochastic dynamic programming framework with some illustrative applications of such a formulation.
Splines and other Metamodels in Reliability Analysis
Authors: Schueremans Luc , Van Gemert Dionys
Published: February 2008

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.
Calibration
Authors: Bzik Thomas J.
Published: February 2008

Calibration establishes and standardizes common units of measurement for commerce and scientific study. Through calibration the measured item or sample is related to items or samples with known properties, the standards. Statistical methods are typically used to identify and quantify the form of the relationship between sample measurements and one or more standards. They are also used to evaluate the accuracy and precision of the current as well as alternative measurement methods. Statistical tools are also used to determine the optimal frequency of recalibration as well as the frequency with which calibration should be checked. Calibration models can take the form of linear relationships or a nonlinear relationship. Two‐variable calibration models are often monotonic in their form. While many calibration models often involve only two variables, they can be multivariate in nature. Keeping bias out of calibration models requires not only selecting the correct model form but also ensuring that the model is fit, using an appropriate optimization criteria. Calibration models typically provide very high explanatory powers as compared to other typical scientific modeling applications such as plant or process optimization studies. Calibration models also typically yield extremely good levels of statistical significance. Current common statistical toolsets used in calibration studies are often not well structured for handling the actual, and sometimes complex, error structures that occur in routine application of calibration.
Policy Deployment for Performance Improvement
Authors: Watson Gregory H.
Published: February 2008

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).
Classification and Regression Tree Methods
Authors: Loh Wei‐Yin
Published: February 2008

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.
Monitoring of Safety in
EU
Railways
Authors: Pira Angelo , Piazza Roberto , Rajan Jane
Published: February 2008

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.
Mixture Models, Multivariate: Aging and Dependence
Authors: Spizzichino Fabio
Published: February 2008

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.
Statistical Efficiency
Authors: Kenett Ron S.
Published: February 2008

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.
Accelerated Life Tests:Bayesian Models
Authors: Soyer Refik , Erkanli Alaattin , Merrick Jason R.
Published: February 2008

In this article we consider accelerated life test models where the effect of accelerated environments on failure behavior is described by time transformation functions. We present extensions of basic models by introducing hierarchical and dynamic strategies to allow for changes in the time transformation function with the accelerated environment. We develop Markov chain Monte Carlo methods to make Bayesian inferences from these models where no analytical forms are available for the posterior and predictive distributions of interest. We illustrate our approach with two examples and discuss what type of insights can be obtained from Bayesian analysis.