bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quality Control, Computing in
Authors: Noguera John 
Published: February 2008

AbstractThe 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.
Density and Failure Rate Estimation
Authors: Müller Hans‐Georg , Wang Jane‐Ling 
Published: February 2008

Abstract
Density and failure rate estimation are valuable tools to assess and explore the occurrence and timing of failures in reliability and quality control. This article focuses on nonparametric approaches such as histograms, the kernel method, and other smoothing procedures. Nonparametric density and failure rate estimation is particularly valuable for exploratory data analysis and in situations where available information is insufficient to specify a parametric model, as these methods “let the data speak for themselves”, and no assumptions are needed beyond smoothness of the functions to be estimated. Issues specific for nonparametric approaches are finite bias behavior and choice of the necessary smoothing parameter. For failure rate estimation, available data are sometimes incompletely observed (censored) or available in aggregated form only, necessitating appropriate adjustments. Shape restrictions such as increasing failure rate (IFR) are also occasionally of interest in reliability.
M
onte
C
arlo Methods, Univariate and Multivariate
Authors: Walsh Cathal D. 
Published: February 2008

Abstract
With the prevalence of high‐powered computing, Monte Carlo methods are increasingly used in a wide range of disciplines. In this article we review some of the principles behind these methods, note the relationship with other techniques used in systems reliability, and demonstrate some methods in practice. In particular, the accept–reject algorithm, the importance sampler, the method of compositions and the specific case of Box–Muller transformation are highlighted here.
Failure Modes and Effects Analysis, Implementation of
Authors: Bowles John B. 
Published: February 2008

AbstractFailure modes and effects analysis (FMEA) is potentially one of the most beneficial and productive tasks in a well structured reliability program. FMEA consists of examining the modes and causes of potential item failures and determining the product response to the failure. Steps can then be taken to change the design to eliminate the failure, mitigate its effects, or develop compensating provisions if the failure should occur. FMEA can be applied to hardware, software, material, and process related causes of failure. Functional, interface, and detailed FMEAs can be usefully employed throughout the design process from the concept stage to production and deployment.
Fault Tree Analysis for Large Systems
Authors: Andrews John D. 
Published: February 2008

Abstract
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.
Integrating Computer and Physical Experiment Data
Authors: Williams Brian J. , Higdon David M. 
Published: February 2008

Abstract
The investigation of complex physical systems utilizing sophisticated computer models has become commonplace with the advent of modern computational facilities. In many applications, experimental data on the physical systems of interest is extremely expensive to obtain and hence is available in limited quantities. The mathematical systems implemented by the computer models often include parameters having uncertain values. This article provides an overview of statistical methodology for calibrating uncertain parameters to experimental data. This approach assumes that prior knowledge about such parameters is represented as a probability distribution, and the experimental data is used to refine our knowledge about these parameters, expressed as a posterior distribution. Uncertainty quantification for computer model predictions of the physical system are based fundamentally on this posterior distribution. Computer models are generally not perfect representations of reality for a variety of reasons, such as inadequacies in the physical modeling of some processes in the dynamic system. The statistical model includes components that identify and adjust for such discrepancies. A standard approach to statistical modeling of computer model output for unsampled inputs is introduced for the common situation where limited computer model runs are available. Extensions of the statistical methods to functional outputs are available and discussed briefly.
M
arkov Chain
M
onte
C
arlo, Introduction
Authors: Wiper Michael P. 
Published: February 2008

Abstract

In this article, we introduce the basic ideas of Markov chain Monte Carlo simulation. We start by briefly commenting on the main ideas of Monte Carlo sampling and then by reviewing the theory of Markov chains, concentrating in particular on the conditions required for the existence of a stationary distribution. We then introduce the Metropolis–Hastings algorithm and show that this generates a Markov chain with a specific stationary distribution. We next introduce some specific ways of implementing the Metropolis–Hastings algorithm,
via
the independence sampler, the Metropolis sampler and the Gibbs sampler among others. Finally, we comment on how the convergence of a Markov chain to equilibrium can be assessed in practice and we finish with an example.
Laplace Approximations inBayesian Lifetime Analysis
Authors: Onar Arzu 
Published: February 2008

AbstractWe provide some background on Laplace approximations based on the work by Tierney and Kadane and illustrate the method's use in obtaining posterior distributions as well as the associated parameter estimates in Bayesian lifetime analysis. The advantages and the disadvantages of using this approach compared to other numerical alternatives are also discussed. Finally, a few examples from the relevant literature where the approximation has been successfully employed are provided.
Neural Networks in Statistical Process Control
Authors: Hwarng H. Brian 
Published: February 2008

AbstractThe 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.
Decision Search via Simulation
Authors: De Iorio Maria 
Published: February 2008

AbstractThis entry provides an overview of simulation‐based methods in decision theory and optimal design problems. We consider decision problems defined by a utility function and an underlying probability model for all unknowns. The search for the optimal design requires the maximization of the expected utility with respect to some design parameter, where the expectation is taken with respect to all the unknowns. In many applications the solution to the optimal design problem is not analytically tractable, as neither the maximization nor the integration have a closed‐form solution requiring approximation and/or simulation‐based methods. We present four related simulation‐based strategies: prior simulations, curve fitting of Monte Carlo experiments, Markov chain Monte Carlo simulations in an augmented probability model, and inhomogeneous Markov chain simulations.
Lifetime Distributions, Optimization Methods for
Authors: Hayes Kevin , Barry Don 
Published: February 2008

Abstract
This article introduces the likelihood function as the main tool for parameter estimation and statistical inference with lifetime distributions. When the likelihood function cannot be maximized directly, numerical optimization is required. Approaches based on the Newton–Raphson algorithm are discussed and modifications used to ensure properties such as convergence to maxima rather than minima or saddle points, calculation of nonnegative parameter estimates, and invertibility of the Fisher information matrix are considered. The variability of parameter estimates, generalized linear models, and stochastic search routines are also briefly mentioned.
Bootstrap and Jackknife, Overview
Authors: Martin Michael A. , Roberts Steven 
Published: February 2008

AbstractA 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.
Resampling for Lifetime Distributions
Authors: Belyaev Yuri K. 
Published: February 2008

Abstract

In many applications, data sets contain lifetimes of occurred events, e.g., times of failures, diagnoses, censorings, and so on. If we want to use an estimator of a lifetime characteristic of interest, then it is necessary to evaluate the accuracy of this estimator. Resampling methods can be efficiently used to evaluate the accuracy of many estimators if the size of the data with lifetimes is sufficiently large. They are feasible for realization on personal computers. Correctly applied resampling methods give asymptotically consistent evaluation of the accuracy of estimators under certain assumptions as the size
n
of the data unboundedly grows. The basic tools of mathematical statistics, the likelihood and the central limit resampling theorem, are used to justify resampling methods.
Life Distributions, Simulation of
Authors: Marseguerra Marzio , Zio Enrico 
Published: February 2008

Abstract
This chapter introduces the principles underlying the simulation of the probability distributions that typically characterize the life or failure processes of engineered components and systems. The basic procedures for sampling random numbers are reviewed and examples are provided with reference to the uniform, exponential, and Weibull distributions, which are among the most commonly used probability distributions in reliability, availability, and safety analysis.
Simulation of the Beta‐Stacy Process
Authors: De Blasi Pierpaolo 
Published: February 2008

AbstractThis article reviews simulation methods for the beta‐Stacy process, which belongs to the family of neutral‐to‐the‐right (NTR) processes used in Bayesian nonparametric inference on survival distributions. The definition and properties of the NTR processes are recalled, with a view to represent the continuous component of the process at the integrated hazard levelviathe Lévy measure. The conditional distribution of the prior process, given right‐censored data, is described; this represents the key ingredient for devising sampling schemes. Four methods aiming at the generation of increments or trajectories of the hazard rate process are revised. We explain the ideas behind each of them and sketch the correspondent algorithms for the posterior beta‐Stacy process.
Dirichlet Process, Simulation of
Authors: Laud Purushottam W. , Pajewski Nicholas M. 
Published: February 2008

Abstract
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.
Optimal Reliability Design‐Modeling
Authors: Kuo Way , Wan Rui 
Published: February 2008

AbstractReliability has become a greater concern in recent years because high‐tech industrial processes with ever increasing levels of sophistication comprise most engineering systems today. To keep pace with this rapidly developing field, this chapter provides a broad overview of recent research on reliability optimization problems. In particular, we address issues related to:UGF‐based optimal multistate system (MSS) design;percentile life employed as a system performance measure;multiobjective optimization of reliability systems, especially with uncertain component reliability estimations;innovation and improvement in traditional reliability optimization problems, such as fault‐tolerance mechanism and cold‐standby redundancy involved system design.The chapter concludes with a discussion of future challenges related to reliability optimization.
Expectation Maximization Algorithm
Authors: Gormley Isobel C. , Murphy Thomas B. 
Published: February 2008

AbstractThe expectation maximization (EM) algorithm is a powerful tool for finding maximum‐likelihood estimates in problems where the data are incomplete. Missing data may be an intrinsic feature of the problem or may be artificially imputed to ease estimation. The EM algorithm is an iterative algorithm that consists of two steps—an expectation step (E‐step) and a maximization step (M‐step). The output is a sequence of parameter estimates that converge to a maximum‐likelihood estimate. The implementation of the EM algorithm is illustrated using a finite mixture of exponential densities.
Data Mining, Evaluation Techniques in
Authors: Gür Ali Özden F. 
Published: February 2008

Abstract

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.
Polya Trees and Their Use in Reliability and Survival Analysis
Authors: Hanson Timothy E. 
Published: February 2008

AbstractThe nonparametric Polya tree prior generalizes traditional parametric models such as the Weibull family. Relative to other well‐known priors, such as Dirichlet, gamma, and beta processes, Polya trees have seen more limited use. However, the Polya tree prior has several attractive features, including the existence of survival densities and the ability to be incorporated in different semiparametric models.
K
ano Analysis
Authors: Shenk Debra 
Published: February 2008

Abstract
In the early 1980s, Dr. Noriaki Kano of Tokyo Rika University introduced several concepts aimed at a better understanding of quality as the customer defines it. He described a continuum over which a customer assigns a valuation to a particular feature or attribute of a product or service. These concepts include the “attractive quality” and “must‐be quality”. When properly implemented, an understanding of these methods can lead companies to define sets of features that will fulfill customer needs and grow market share.
Change Management, Stakeholder Assessment in
Authors: Trabert Thomas F. 
Published: February 2008

Abstract
In managing organizational changes, a stakeholder assessment tool is used to increase the chances of a successful outcome. The benefit of a stakeholder assessment is to understand a stakeholder's level of support for a particular change initiative by first, determining his level of involvement in and commitment to the change and then assessing his ability to impact that change through his influence, authority, and control in the organization. The results of these two assessments of each stakeholder are summarized in a matrix showing the relationship of each stakeholder with the others. For stakeholders who support the change, a plan is created to increase their level of influence throughout. For those who are not supportive of the change, a plan is created to either gain their support or decrease their influence.
Bias in Modeling and Monitoring Health Outcomes
Authors: Grigg Olivia A. J. 
Published: February 2008

Abstract
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.
Repair Data, Sets of: How to Graph, Analyze, and Compare
Authors: Nelson Wayne B. 
Published: February 2008

Abstract
Many products accumulate repeated repairs and repair costs over time. Analysis of such recurrence data requires special statistical models and methods not covered in basic reliability books. This article presents a simple and informative model and plot for analyzing data on numbers or costs of repeated repairs of a random sample of units. The plot is illustrated with transmission repair data from preproduction cars on a track test. This article also presents a method for comparing two such datasets, illustrated with automatic and manual transmissions. Computer programs that calculate and make the plots and comparisons with confidence limits are surveyed.
Contingency Table, Square
Authors: Lipsitz Stuart R. 
Published: February 2008

AbstractThis article discusses the analysis of contingency tables for which the number of rows equals the number of columns. Three types of models for such tables are discussed: marginal models for comparing margins of the table, log linear models for describing the joint distribution and the association patterns, and conditional models that consider the distribution of one of the categorical variables conditional on the outcome for the other one. Other topics discussed include testing marginal homogeneity and symmetry, the models of quasisymmetry and quasi‐independence, and modeling agreement.
Contingency Table
Authors: Altham P.M.E. 
Published: February 2008

Abstract

Contingency tables give counts for cross classifications of categorical variables. This article illustrates two‐dimensional tables (two variables). It then discusses the hypothesis of independence and presents χ
2
tests. An example of quasi‐independence is presented, and small‐sample tests are mentioned. Extensions to multiway contingency tables and the relevant hypotheses for such tables are also discussed. Loglinear models describe the hypotheses, and there are corresponding logistic regression models when one of the variables is a binary response variable.
Brainstorming
Authors: Plsek Paul E. 
Published: February 2008

Abstract
Brainstorming is a group process for generating ideas on a selected topic. It can be used in a variety of applications in quality and reliability work whenever a diverse set of original ideas, approaches, theories, or points of view is desired. Brainstorming builds on classic research into the traits of highly creative groups by providing practical rules and a model for use in organizational group settings.
Laboratory Maintenance of Neisseria gonorrhoeae
Authors: Spence Janice M. , Wright Lori , Clark Virginia L. 
Published: February 2008

AbstractNeisseria gonorrhoeae is a human pathogen of mucosal surfaces, thus laboratory manipulations must include appropriate safety measures. The growth requirements and behavior of the gonococcus are significantly different from many bacteria, necessitating modifications of common laboratory techniques. A fastidious organism, N. gonorrhoeae requires enriched media in a CO2 atmosphere at 35° to 37°C for growth. In addition, N. gonorrhoeae expresses potent autolysins whose activity increases following glucose depletion during stationary phase, leading to cell death. Long believed to be an obligate aerobe, the gonococcus is capable of anaerobic growth when provided with a suitable electron acceptor. This unit provides information for both aerobic and anaerobic growth, basic long‐term and daily maintenance of gonococcal cultures, as well as safety considerations for laboratory studies. Curr. Protoc. Microbiol. 8:4A.1.1‐4A.1.26. © 2008 by John Wiley & Sons, Inc.
Lymphocytic Choriomeningitis Virus (LCMV): Propagation, Quantitation, and Storage
Authors: Welsh Raymond M. , Seedhom Mina O. 
Published: February 2008

AbstractLymphocytic choriomeningitis virus (LCMV) is an enveloped, ambisense RNA virus and the prototypic virus of the arenavirus group. It can cause viral meningitis and other ailments in humans, but its natural host is the mouse. The LCMV/mouse model has been useful for examining mechanisms of viral persistence and the basic concepts of virus‐induced immunity and immunopathology. This unit discusses strain differences and biosafety containment issues for LCMV. Recommendations are made for techniques for propagating LCMV to high titers to quantify it by plaque assay and PCR techniques and to preserve its infectivity by appropriate storage. Curr. Protoc. Microbiol. 8:15A.1.1‐15A.1.11. © 2008 by John Wiley & Sons, Inc.