bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gauge Repeatability and Reproducibility (
R
&
R
), Variance Components in
Authors: Borror Connie M. , Burdick Richard K. , Montgomery Douglas C. 
Published: February 2008

Abstract

Traditionally, a standard gauge repeatability and reproducibility (R&R) study consisted of a two‐factor crossed design with operators and parts; with each operator measuring each part say
r
times. There are numerous situations where there are more than two factors, the design is not crossed but possibly nested, or the appropriate design is crossed and nested. In this article, variance components for gauge R&R studies are presented for these more complex experimental situations.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Misclassification Rates
Authors: Burdick Richard K. 
Published: February 2008

Abstract
To properly monitor and improve a manufacturing process, it is necessary to measure attributes of the process output. Measurements of these attributes are taken by a gauge that has associated measurement variability. Gauge R&R studies are used to determine if the measurement system variability is harmfully large. The approach for evaluating the goodness of the measurement system based on a gauge R&R study is to determine the probability that a part is misclassified. The better a measurement system, the less likely it is to classify a “good” part as “bad”, and a “bad” part as “good”. References are provided for computing bounds on the probabilities of these misclassifications.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Confidence Intervals for
Authors: Chiang Andy Kok Leong 
Published: February 2008

Abstract
Gauge repeatability and reproducibility (R&R) studies are often conducted to determine the capability of a gauge for monitoring a manufacturing process. In this paper, we discuss common measures of gauge capability as well as established confidence intervals for them, based on the modified large sample and generalized confidence interval methods.
Measurement Systems Analysis, Capability Measures for
Authors: Larsen Greg A. 
Published: February 2008

Abstract
There are two “families” of measurement capability metrics in common use. There are those that compare measurement variation to total or part variation, and there are those that compare to the tolerance or specification width. These capability metrics are all functions of the variance components in the statistical model used to describe the response of interest. In addition to these capability metrics, it is also often useful to estimate the misclassification rates that arise from using the measurement system to discriminate between “good” and “bad” items being tested. An additional capability metric is obtained from the ratio of the estimated misclassification rates to the rates that would occur purely due to random chance. This section will define several capability measures, how they are related to one another, and give some common criteria for capability. Additional topics discuss the effect of the unit of measure and present a situation where none of the aforementioned metrics are particularly useful in quantifying the capability of a measurement process.
Gauge Repeatability and Reproducibility (
R
&
R
) Studies, Destructive Testing
Authors: Trip Albert , De Mast Jeroen 
Published: February 2008

Abstract

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

Abstract

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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
Modules and Modular Decomposition
Authors: Zuo Ming J. 
Published: February 2008

Abstract
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

Abstract

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”!
Repairable Systems: Statistical Inference
Authors: Agustin Ma. Zenia N. 
Published: February 2008

AbstractThis 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

Abstract
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.
Accelerated Life Tests: Classical Methods for Design and Analysis
Authors: Giorgio Massimiliano 
Published: February 2008

Abstract
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

AbstractIn 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

AbstractMost 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

AbstractEffectively 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

Abstract

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

Abstract

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

Abstract

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.