bluuebunny/crossref_metadata_2025_split huggingface dataset:
Process Capability Indices, Nonnormal
Authors: Chou Youn‐Min , Polansky Alan M. 
Published: February 2008

AbstractData from manufacturing processes may often come from nonnormal distributions. Process capability indices for nonnormal distributions have attracted much research in the last 20 years. This paper presents process capability indices for nonnormal distributions and reviews various approaches to deal with the problem of nonnormality in evaluating process capability.
Process Capability Indices, Nonparametric
Authors: Polansky Alan M. 
Published: February 2008

Abstract
The capability of a manufacturing process is a measure of the ability of the process to consistently produce items within engineering specifications. Most early attempts to assess the capability of a process were based on the assumption that the quality characteristic of interest follows a parametric distribution, usually normal. Several studies of process data indicate that many quality characteristics follow nonnormal distributions.Further, theoretical and empirical studies of measures of process capability indicate that violations of the assumption relating to the distribution of the quality characteristic can produce serious errors in conclusions about the capability of the process. This article reviews methods that have been developed to assess process capability within a nonparametric framework. Such methods require few specific assumptions about the distribution of the quality characteristic. The methods reviewed in this article include those based on nonparametric tolerance limits, nonparametric estimates of the process yield, and indices based on the percentiles of the process distribution.
Statistical Process Control, Multivariate
Authors: Fuchs Camil , Kenett Ron S. 
Published: February 2008

AbstractOwing to pervasive capabilities of modern data collection, analysis, and graphical displays, the multivariate statistical process control is playing an increasingly central role in industry and services. Its application requires careful consideration of the data context and the statistical tools involved. In this section we present various situations in which such techniques are used and provide details on their practical implementation.
Process Capability Indices,
B
ayesian Estimation of
Authors: Spiring Fred , Cheng Smiley 
Published: February 2008

Abstract
Practitioners often base inferences regarding the capability of a process on a point estimate of one of the common indices without examining the distributional properties of the estimator used. The practice is due, in part, to (a) the complexity of the indices developed and, at least initially, (b) the lack of stochastic development in the general area of process capability indices. Since the mid‐1980s the body of work addressing (a) estimation of and (b) drawing inference from process capability indices has grown steadily. Investigating process capability from a Bayesian perspective has mirrored this development. Much like the indices themselves, the Bayesian approach has seen two distinct patterns develop. One focus has been on examining parts nonconforming while a second examines the ability of a process to cluster around a target.
Process Capability Plots
Authors: Vännman Kerstin 
Published: February 2008

Abstract
A process is usually defined to be capable if a process capability index exceeds a stated threshold value. This definition can be expressed graphically as a region in the plane, defined by the process parameters, to obtain a process capability plot. Under the assumption of normality, safety regions can be plotted in the process capability plot to draw conclusions about process capability at a given significance level. Alternatively, an estimated process capability plot defined by the estimators of process parameters can be used. This article discusses these different kinds of plots and presents examples and interpretations.
Process Yield
Authors: Lin Pi‐Chuan , Pearn Wen Lea 
Published: February 2008

Abstract

Process capability indices (PCIs), establishing the relationship between the actual process performance and the manufacturing specifications, have been the focus of recent research in quality assurance and capability analysis. In this article, PCIs dedicated to the measurement of the yield of normally distributed processes with two‐sided specification limits and one‐sided specification limit are discussed. The yield measure formulas based on
C

pk

,
C

pm

,
C

pmk

,
S

pk

,
C

PU

, and
C

PL

are all displayed. The corresponding upper bounds of nonconforming parts per million (NCPPM) for various values of
C

pk

,
C

pm

, and
C

pmk

and the corresponding NCPPM for various
S

pk

values are tabulated.
Measurement Systems Analysis, Attribute
Authors: Borror Connie M. 
Published: February 2008

Abstract
Methods for assessing the capability of a quantitative or continuous measurement system are well documented and include the tabular method and the analysis of variance approach. When the measurement system involves attribute data, the standard quantitative methods are not appropriate. An attribute gauge measurement system is one where the parts or objects under study are placed into one of two or more possible categories. The classification of the part is the measurement of interest in an attribute measurement system. Several commonly used statistics for “appraiser agreement” and attribute gauge repeatability and reproducibility are presented.
Measurement Systems Analysis, Overview
Authors: Michelson Diane K. 
Published: February 2008

Abstract

Measurement systems analysis is a way to quantify the variability of a metrology process. The fundamental technique is the gauge
repeatability and reproducibility
(GR&R) study, which yields an estimate of

. The estimate is then used to determine whether or not the metrology system is capable of measuring the process under study. The stability of the measurement system is also determined.
Fisheries Stock Assessment
Authors: Maxwell David L. , Pilling Graham M. 
Published: February 2008

AbstractFishing is an economically and socially important activity with around 90 million tonnes of wild fish caught each year. Fisheries policy tries to balance potentially conflicting management drivers for sustainability and maintenance of ecosystem biodiversity with socioeconomic drivers of profit and employment at a variety of geographic scales. Stock assessment's role is to provide the best possible technical support to this process, estimating current and possible future fishery states and the effects of management decisions. This article summarizes the assessment process from the collection of biological and fisheries data, through the mathematical modeling approaches used, to assessing the uncertainty of the results and the robustness of advice. At each stage we highlight quality assurance issues and provide key references for more detailed study.
Sampling Sediments in the Marine Environment
Authors: Moffat Colin F. , Webster Lynda , Pollard Pat , Ahmed Abdulwaheed S. , Russell Marie , Davies Ian M. 
Published: February 2008

Abstract
A number of studies into sampling regimes for marine sediments are discussed. The use of random sampling, stratified random sampling, and the combining of samples are all investigated in coastal and offshore environments. The resulting data is analyzed using standard statistical methods. Carefully designed sampling regimes, with a sound statistical basis, will result in data which can be used to describe a defined area or else make comparisons of contaminant loadings between different areas, often at a reduced cost.
Healthcare Performance Reports
Authors: Bevan Richard G. 
Published: February 2008

Abstract

This article is about the impacts of reporting healthcare performance within a country and confidential reports to individual clinicians as part of an audit process. It considers three approaches to reporting information: the “soft” approach, which is about quality improvement, focuses on success, and is voluntary; the “hard” approach, which is compulsory and about quality assurance; and the market approach, which relies on corporate or consumers' choices of health plans, and corporate or patients' choices of providers. It considers the impacts of reporting healthcare performance on four different groups: third party payers (either corporately or by consumers), patients, physicians, and providers. The evidence is that although publication of results has an impact, this is on providers and not
via
actions of patients or third party payers. The article suggests criteria that influence this impact and outlines statistical implications.
Disease Surveillance
Authors: Lawson Andrew B. 
Published: February 2008

Abstract
This work focuses on disease surveillance and issues related to prospective detection of changes or aberrations in health data. One feature of recent interest in this area is the use of ancillary variables to allow for early detection of outbreaks. For example, gastro‐intestinal disease outbreaks can be detected by examining over‐the‐counter sales of pharmaceutical products. This work is sometimes known as syndromic surveillance. The focus of the detection will define the correct methods used in detection. For example, if a cluster of risk is to be detected then different methods would be used that if a jump in risk over time were to be detected.
Self‐Controlled Case Series Analysis
Authors: Hocine Mounia N. , Whitaker Heather J. , Farrington C. Paddy 
Published: February 2008

Abstract
The case series method was developed by Farrington 1 to investigate the strength of association between a time‐varying exposure and a potentially recurrent adverse event, using cases only. The method has been widely used in pharmacoepidemiology, particularly in the study of vaccine safety. The method is derived from an age‐dependent Poisson model by conditioning on the number of events and on exposure histories. The conditioning leads to estimation within individuals, and thus the effects of fixed covariates cancel out. In some circumstances, the case series method has high efficiency, relative to the cohort method. A semiparametric version of the case series method, in which the underlying age effect is left unspecified, is also available.
Quality in Critical Care Medicine
Authors: Segal Eran 
Published: February 2008

Abstract
Critical care medicine is a growing element of modern hospital medicine. Critically ill patients consume about of 20% of hospital budgets and are therefore a very expensive part of hospital medicine. The occurrence of errors in ICU practice is determined by many factors including ICU design, workflow parameters, and the type of clinical procedures and patient population. Newer approaches to reducing error and improving expected outcome in the treatment of critically ill patients require monitoring and measurement of process, as well as of specific clinical outcomes such as the incidence of line related infections, ventilator associated pneumonia and also compliance with the tools needed to improve care. A great deal of research is directed at improving clinical care and outcome using measurements and improving treatment accordingly. Some information is available that ICUs which utilize these tools may provide better outcome.
Disease and Clinical Trial Modeling
Authors: Abbas Ismail , Rovira Joan , Cobo Erik , Romeu Joan , Casanovas Josep 
Published: February 2008

Abstract

Introduction
The objective is to show how disease and clinical trial modeling can be applied to estimate the success probability, duration and cost that resulting from a certain trial design or from similar decisions.

Methods
Multidisplinar team has participated in the construction of the general simulation model of clinical trials. This model is constructed splitting the whole process into two main parts. The first part represents the recruitment sub‐model aiming at estimating the time and the number of included patients. The second part is based on the follow‐up sub‐model represented by disease progression.

Results
The model is applied to retrospective clinical trial.

Conclusion
Simulation modelling will optimise clinical trials whenever information from different disciplines, such as clinics, statistics, economics and informatics is integrated and used properly.
Statistical Process Control in Clinical Medicine
Authors: Winkel Per , Zhang Nien Fan 
Published: February 2008

Abstract
A control chart may be used to characterize a clinical process and subsequently to monitor it. The chart depicts the values of a measurable quantity characterizing the process, e.g., the fraction of patients dying per month during a specified surgical procedure.
When the results of the process are stable the quality of the process may be assessed, e.g., by estimating the fraction of results that do not have an acceptable quality.
As an example, it is shown how the daily average of patient waiting times at an outpatient clinic is adjusted and subsequently monitored. If the variable one wants to monitor is an outcome measure, risk adjustment is necessary in order to adjust for variation between the patients in terms of severity of disease, comorbidities, age, sex, etc.
The outcome results of several healthcare providers (e.g., hospital departments) may be compared if the patients are assigned at random to the providers. Using simulation it is illustrated how an unknown or unmeasured risk factor may bias the comparison of the quality of care between two healthcare providers if observational data are used. It is recommended that risk‐adjusted comparisons based on observational data are considered inconclusive and used as inspiration for further investigations.
k

‐out‐of‐

n

Systems
Authors: Shepherd Deborah K. 
Published: February 2008

Abstract

A
k
‐out‐of‐
n
system is a system with
n
components which functions or works if at least
k
out of the
n
components work. Parallel and series systems are special cases of a
k
‐out‐of‐
n
system. There are many different arrangements of the components in a
k
‐out‐of‐
n
system and many assumptions that can be made about the lifetimes of the components. In this chapter, the lifetimes of the components in the system are assumed to be independent and identically distributed. The reliability function of the system is found by calculating the probability that at least
k
components out of the
n
components work. The mean time to failure of the system is also found for the case of components with exponential distributions of lifetimes. In addition, the reliability function for a linear and circular consecutive
k‐out‐of‐n
system is given.
Component Reliability Importance
Authors: Rigdon Steven E. 
Published: February 2008

Abstract

The concept of reliability importance gives a measure of how much changes in the reliability of a particular component of a coherent system affects the reliability of the system. The definition of the importance of component
k
is
I
(
k
) = ∂
r
(

p

)/∂
p

k

, where

p

is the vector of component reliabilities and
r
(

p

) is the system reliability function. Examples of the reliability importance for common systems are given. The structural reliability importance is also given, along with its relationship to the reliability importance.
Reliability of Redundant Systems
Authors: Pham Hoang 
Published: February 2008

AbstractThis articles provides a brief concept of how to evaluate the reliability of redundant systems with various aspects including dynamic redundancy, imperfect coverage, fail‐safe redundancy, noncoherent systems, degradable redundancy, multiple failure modes,k‐out‐of‐nsystem redundancy, weighted systems, and load‐sharing redundancy.
Fault Trees
Authors: Bartlett Lisa M. 
Published: February 2008

Abstract
The ability to analyze how failures occur and determine their likelihood of occurrence is fundamental in maximizing the functionality of a system. To achieve this, a technique such as fault tree analysis is required. The technique can be used to carry out a qualitative analysis, namely, identifying the failure events that contribute to the system failure, and a quantitative analysis, calculating numerical statistics relating to the system, for example, the probability of failure. This article provides an introduction to the technique and its analysis potential.
System Reliability:
M
onte
C
arlo Estimation
Authors: Rigdon Christopher J. , Rigdon Steven E. 
Published: February 2008

Abstract

Monte Carlo methods are useful for a number of situations in reliability. First, for very complex systems, it is difficult to determine the system reliability even if the component reliabilities are known. This can be done by simulating the performance (operating/failed) of each component, and determining the status of the system given the performances of these components. Second, the system reliability can be estimated when component reliabilities are estimated from pass/fail data. Uncertainties in the resulting estimate of system reliability can be obtained
via
Monte Carlo simulation. Third, many questions of availability (or other measures of system performance) can be treated using queuing theory. Since queuing models can become intractable, especially when times between events or repair times are not exponential random variables, Monte Carlo methods can be used.
System Reliability: Computational Algebra Methods
Authors: Leemis Lawrence M. 
Published: February 2008

Abstract

Computer algebra systems are useful in calculating system reliability. We use several diverse examples to illustrate how a computer algebra system can be helpful in determining system reliability. The examples presented here are (a) analyzing a
k
‐out‐of‐
n
system; (b) determining the mean time to failure of a parallel system with symbolic parameters; (c) determining the conditional survivor function for a system with a piecewise survival distribution; (d) determining the probability of mission success for a unit and two cold standby units; (e) determining a lower confidence bound on the system reliability from binary failure data collected on each of the independent components in a coherent system
via
bootstrapping; and (f) determining the exact distribution of a popular goodness‐of‐fit test statistic.
Genetic Algorithms in Reliability
Authors: Coit David W. , Taboada Heidi A. 
Published: February 2008

Abstract
Genetic algorithms (GAs) have been effectively used to solve many difficult engineering problems and are particularly effective for solving combinatorial optimization problems with large, complex search spaces. The redundancy allocation problem (RAP) is perhaps one of the most common problems in design for reliability and it is known to be NP hard. Redundancy is used as a strategy to meet high reliability specifications in many system design engineering projects. In this material, we present an overview of some of the most relevant research that has been done on the use of GAs in the reliability optimization of redundant systems.
Reliability, Safety, and Risk Management
Authors: Cox Sue 
Published: February 2008

AbstractThe aim of this paper is to present a brief exposition on the connection between safety and reliability. The author adopts an approach that is described in full in the bookSafety, Reliability and Risk Management: An Integrated Approach. This book was designed to support students and practitioners in the development of their understanding of safety and its links to reliability. The text is structured around a systems approach to safety, reliability, and risk management and highlights a number of practical tools that practitioners have used to identify and manage risk. This approach is compatible with the current development of risk management theory, both in relation to public decision making and loss prevention strategies.
System Downtime Distributions
Authors: Pievatolo Antonio 
Published: February 2008

Abstract
System downtime distributions are useful for determining economic or safety performance measures of a system. We consider system downtime in an interval and after a failure. We try to give expressions that can be readily programmed using only information on failure and repair time distributions of every single component of the system. For this reason, all such expressions are of asymptotic nature. We deal with systems of independent binary components in detail, whereas we mention only briefly (also indicating references) the tools that can be used for systems with multistate components.
Intensity Functions for Nonhomogeneous Poisson Processes
Authors: McCollin Christopher 
Published: February 2008

AbstractWhen analysing reliability data from a repairable system e.g. a car, a fridge, etc, reliability specialists are interested in the full life history of a system so that they may identify problems that have affected its life and apply solutions, if possible, to other new systems so that these new systems do not suffer from the same problems. The basis of reliability analysis is to understand how systems are failing by modeling their life history so that prediction of the likely future reliability of systems is possible. This is the basis of reliability growth (development) testing where systems under development are tested to find latent design and manufacturing defects so that they can be eliminated from the full‐scale production system.The simplest way of identifying structure in the system's life history is to plot the data. Three plots are commonly used: plotting failures on a time line; plotting the failure number against cumulative time, and plotting an estimate of the intensity, the rate of occurrence of failures (ROCOF) against cumulative time. These plots appear in Ascher and Feingold [31] and Crowderet al. [61] among others. These plots will help to identify structure such as an increase or decrease in the interfailure times of a system over time (more commonly known as reliability growth and reliability decay); evidence of cycles in the data—these may be short or long cycles; periods of high or low reliability (again related to usage); gaps in the time stream (due to change in the reporting mechanism, management, etc) and outliers (possible abnormal operating conditions).This review lists the intensity functions and their associated expected number of failures, which is the mean value parameter of the nonhomogeneous Poisson process (NHPP). The simplest and best known intensities, those based on mortality rates, those based on hazards of well‐known probability distributions, the software related intensities, and those based on differential equations and extensions are discussed under their appropriate classification headings.
Repairable Systems:
B
ayesian Analysis
Authors: Rigdon Steven E. 
Published: February 2008

Abstract

The Bayesian approach to analyzing data from repairable systems is presented in this chapter. Specifically, the homogeneous Poisson process and the power law process, a nonhomogeneous Poisson process with intensity equal to λ(
t
) = (β/θ)(
t
/θ)
β−1
,
t
> 0, are studied. Other models, such as the renewal process, follow a similar pattern. Also, measures of system performance, such as availability, are discussed, where the Bayesian approach provides a convenient way to combine uncertainties. For multiple copies of a repairable system, we discuss empirical Bayes and hierarchical Bayes methods.
Flowgraph Models
Authors: Huzurbazar Aparna V. 
Published: February 2008

Abstract
Flowgraph models are used to analyze complex multistate systems. The focus is on modeling data from semi‐Markov processes. Semi‐Markov models play an important role in the analysis of time‐to‐event data. However, in practice, data analysis for semi‐Markov processes can be quite difficult and many simplifying assumptions are made. Flowgraph models are multistate models that provide a data analytic method for semi‐Markov processes. The system data can be both censored and incomplete. Flowgraphs are useful for estimating Bayes predictive densities, predictive reliability functions, and predictive hazard functions for waiting times of interest in the presence of censored and incomplete data.