bluuebunny/crossref_metadata_2025_split huggingface dataset:
B
ayesian Control Charts
Authors: Colosimo Bianca M. 
Published: February 2008

Abstract
Control charts are widely used in industrial practice to check for process stability. The basic procedure of control charting is due to Shewhart and consists in periodically inspecting a sample of quality characteristics to decide whether the process is “out of control”. The out‐of‐control state is characterized by deteriorated performances and should be detected as soon as possible to bring the process back to its nominal or “in‐control” state. With reference to this objective, different Bayesian control charts have been proposed in the literature; the term “Bayesian” is used here to denote any approach that adopts Bayes' theorem within the monitoring procedure. In particular, the literature on Bayesian control charting is characterized by two streams of research. The first assumes that all the information required to detect the degraded state is enclosed in the posterior probability that the process is out of control. The second stream of research adopts a more classical perspective, which bases the out‐of‐control rule on detecting changes of the process parameters. In this case, Bayes' theorem allows one to compute the posterior distribution of the parameter of interest. The article reviews approaches proposed in both these streams of research, focusing mainly on the first type of Bayesian procedure.
Degradation Processes
Authors: Lehmann Axel 
Published: February 2008

Abstract
Many failure mechanisms can be traced to an underlying process and stochastically changing covariates. The degradation process may represent wear, fatigue crack growth, corrosion, or material fatigue in engineering settings, and markers of health status and quality of life data in medical settings. Degradation data can provide much more reliability information than traditional failure‐time data and are particularly useful when the application of traditional reliability models is limited due to rare failures of highly reliable items or due to items operating in dynamic environments. A class of reliability models is considered in which failure is related to degradation due to the competing risks of hard and soft failure.
HierarchicalMarkov ChainMonteCarlo (MCMC) forBayesian System Reliability
Authors: Wilson Alyson G. 
Published: February 2008

AbstractHierarchical models are one of the central tools of Bayesian analysis. They offer many advantages, including the ability to borrow strength to estimate individual parameters and the ability to specify complex models that reflect engineering and physical realities. Markov chain Monte Carlo (MCMC) is a set of algorithms that allow Bayesian inference in a variety of models. We illustrate hierarchical models and MCMC in a Bayesian system reliability example.
Warranty: Usage and Wear Process for
Authors: Wilson Simon P. 
Published: February 2008

AbstractMany products, for which warranties are offered, are subject to wear and this can be a major factor in when the product fails. The process by which wear accumulates is best modeled as a stochastic process. In this article, I describe some stochastic processes for wear. Each process is defined, the ease with which its probability distribution can be computed is described, and its advantages and disadvantages as a wear process is discussed. I briefly discuss how these models may be applied to practical warranty problems such as for products whose warranty is defined in terms of time and some measure of usage (like kilometers traveled) to failure.
Multiattribute Warranty Policies
Authors: Chun Young H. 
Published: February 2008

Abstract
Warranties have been important elements in business planning and decision making for manufacturers of products and producers of services, and this will continue to be the case in years to come. In this article, we introduce various types of single‐attribute and multiattribute warranty policies, and review different modeling approaches with an emphasis on two‐attribute warranty plans. Some suggestions for future research are also provided.
Inspection Policies for Reliability
Authors: Baker Rose D. 
Published: February 2008

Abstract
The rôle of inspections in improving reliability is discussed, and a distinction is drawn between inspections of standby equipment, that “waits to work”, and inspections of equipment such as a production line that is in continuous use. Simple examples of both situations are given, and some of the complexities of the various models used are discussed and referenced. It is stressed that models are valueless for practical purposes without checking that they are appropriate, and without estimation of their parameters.
Group Maintenance Policies
Authors: Wilson John G. , Benmerzouga Ali , Anderson Chris K. 
Published: February 2008

Abstract

This paper introduces a new group replacement policy for
n
independent identical machines operating in parallel. This policy is a generalization of the classes of
m
‐failure, (
m
,
T
) and
T
‐failure policies. The adaptive nature of this policy is its most attractive feature: the replacement rule varies according to the state of the system. Once the rule has been found, implementation is no harder than for other standard policies which do not incorporate this adaptive feature. An attractive feature of this approach is that no other policy that uses only the times of operations of the machines and the number of machines that have failed can do better. It is demonstrated that the classes of
m
‐failure, (
m
,
T
) and
T
‐failure policies are not generally the optimal.
Multivariate Age and Multivariate Renewal Replacement
Authors: Ebrahimi Nader 
Published: February 2008

Abstract

A large part of reliability theory deals with replacement of single‐component systems. Two of the popular replacement policies are (a) age‐replacement and (b) block‐replacement policies. As systems become more complicated and require new technologies and methodologies, more sophisticated maintenance policies are needed to solve the maintenance problems. For systems consisting of
k
related components, a legitimate question is when to replace the components. Obviously, the classical age‐and block‐replacement policies are unsuitable for such situations. This article deals with multivariate versions of age replacement policies that may be used for replacing components. Also, for planning purposes, the article describes several optimal age replacement policies.
Nonparametric Methods for Analysis of Repair Data
Authors: Hollander Myles , Samaniego Francisco J. , Sethuraman Jayaram 
Published: February 2008

Abstract

Repair models specify the joint distribution of the failure times of a system subject to repair. Prominent models include the minimal repair model, the Brown–Proschan model, the Block–Borges–Savits model, the Kijima models, and the general Dorado–Hollander–Sethuraman model. For such models, failure‐time data and data on the types of repairs performed allow inference for the distribution
F
of the time to first failure of the system and other features of the system that can be defined in terms of
F
. This article surveys some nonparametric inference procedures for repair data, including frequentist and Bayesian estimation of
F
, simultaneous confidence bands for
F
, two‐sample tests, tests of the minimal repair assumption, and goodness‐of‐fit tests.
Analysis of Recurrent Events from Repairable Systems
Authors: Agustin Ma. Zenia N. , Agustin Marcus A. , Peña Edsel A. 
Published: February 2008

AbstractThis article provides a review of recently developed dynamic stochastic models for recurrent events, in particular, recurrent events arising from monitoring of repairable systems. Inference methods such as the estimation of model parameters and goodness‐of‐fit testing are also discussed. The procedures described are demonstrated using a reliability data from load–haul–dump machines.
Nonparametric and SemiparametricBayesian Reliability Analysis
Authors: Ghosh Kaushik , Tiwari Ram C. 
Published: February 2008

AbstractIn this article, we first provide an overview of some nonparametric Bayesian methods of inference using life‐history data. These methods include those that use the Dirichlet process, the gamma process, and the beta process as the prior. We then present a semiparametric Bayesian method for estimating the reliability of a component in a multicomponent system using lifetime data from several systems. This method assumes that the component lifetimes have a parametric distribution with a Dirichlet process prior on the distribution of the parameters.
Degradation Models
Authors: Bae Suk J. , Kvam Paul H. 
Published: February 2008

Abstract
Degradation models are used to relate a test item's estimated failure time with the wear and tear during its usage period. From a regression analysis of the degradation data, a test item's lifetime distribution can be inferred. We look at important examples of how an item's degradation rate relates to its lifetime distribution and outline statistical techniques used to analyze degradation data.
Burn‐In and Maintenance Policies
Authors: Mi Jie 
Published: February 2008

Abstract
It is always desired that products have high reliability, long life, and so on. There are basically three phases in which certain measures can be taken to achieve these goals. In the first phase the product has to be carefully designed, good materials have to be used, and so on. This phase will not be considered in this article. In the second phase, i.e. after the completion of production process and before shipment to customers, the burn‐in procedure can be applied to eliminate weak products and thus improve the quality of products before they are shipped. In the third phase, appropriate maintenance measures will keep the products properly functioning for long and accomplish the desired mission with low risk.
Warranty Cost Prediction Based on Warranty Data
Authors: Karim M. Rezaul , Suzuki Kazuyuki 
Published: February 2008

Abstract
This article discusses methods for predicting warranty cost to the manufacturer during a given future value(s) of the variable(s) defining the warranty terms. It models the predicted cost as a function of the lifetime distribution of the product and the average cost per claim based on observed costs of previous claims or costs from previous model years.
Single Sampling by Attributes and by Variables
Authors: von Collani Elart , Göb Rainer , Suresh Kodakanallur Krishnaswamy 
Published: February 2008

Abstract
Single sampling plans excel among statistical product inspection procedures. They are simple in structure, intuitive, and easy for implementation. The study of single sampling plans is illustrative for the understanding of acceptance sampling since single sampling plans exemplify all basic features of acceptance sampling in a particularly simple and transparent instance. Single sampling plans are sometimes theoretically inferior to more refined sampling algorithms like double, multiple, and sequential sampling plans. However, simplicity and ease in implementation have made single sampling the most widely used statistical product inspection procedure.
Rectification Sampling Schemes
Authors: Göb Rainer 
Published: February 2008

Abstract

Statistical sampling plans lead to a decision between accepting or rejecting a lot. Rejection calls for some extraordinary intervention into the routine business flow. A specific type of intervention consists in a more extensive inspection of the lot with the aim of
rectification
, i.e. removing or replacing detected items of inappropriate quality. Sampling schemes with a rectification interpretation of rejection are called
rectification schemes
. The article introduces the procedures and performance measures of rectification schemes. The well‐known
Dodge–Romig rectification scheme
is analyzed in detail.
Continuous Sampling
Authors: Suresh Kodakanallur Krishnaswamy , von Collani Elart 
Published: February 2008

Abstract
This article considers the control of item‐by‐item production in contrast to the control of lot‐by‐lot production. The methods covered are the continuous sampling plan, CSP‐1, some of its extensions and the corresponding MIL‐STD‐1253C, each of them referring to attribute sampling. The situation, in which these methods can be applied, are for example conveyor and moving assembly lines. The mode of operation and the statistical and operational properties of continuous sampling plans are stated.
Skip Lot and Chain Sampling
Authors: Balamurali Saminathan , Jun Chi‐Hyuck , von Collani Elart 
Published: February 2008

Abstract
This article deals with sampling and inspection policies, which are located in the frontier area between process control and lot acceptance sampling. These methods show that from the beginning of statistical quality control the need was felt to consider lot acceptance not as an isolated problem, but in connection with the corresponding lot generating process. However, the methods described here are heuristic ones and do not allow reliable statements about their performance under realistic conditions. Therefore, future research in lot acceptance should focus on modeling the relation between lot quality and process quality for allowing the development of reliable and efficient procedures.
Economic Sampling Schemes
Authors: von Collani Elart 
Published: February 2008

Abstract
The process of globalization has resulted in a situation where an increasing number of companies operate production locations all over the world and especially in the so‐called low‐cost countries. This trend has significantly increased the importance of outgoing and incoming quality inspection, normally by sampling inspection, because in many cases the inspection costs represent a considerable part of the total costs. Despite this situation, there is almost no research done on the application of economic sampling plans. The aim of this chapter is to give a short introduction into the state of the art with respect to economic sampling schemes to initiate further research in this important area of statistical quality control.
Prior Information in Sampling Schemes
Authors: Seidel Wilfried 
Published: February 2008

AbstractThe choice of a sampling scheme and the amount of inspection is guided at least informally by prior information on quality. We are concerned with formal models of prior information. The Bayesian approach is introduced in detail. Based on a prior distribution of submitted lots according to quality and a cost model, a Bayesian sampling plan minimizes the average costs. A special feature of sampling inspection is that the prior distribution (and consequently also posterior distributions and costs) may have a frequentist interpretation (in contrast to a subjective prior distribution). The cost model is not restricted to costs in the monetary sense. It only requires a numerical goal function that measures the consequences of possible decisions, examples are given.Bayesian models may be used as a theoretical instrument for analyzing and comparing the performance of existing sampling schemes. In practice, assumptions like the stability of the prior may not be satisfied and full information on the prior distribution and the costs is often lacking. Modifications of the full Bayesian approach are necessary, including robust strategies and Γ‐minimax. Estimation of the prior distribution from past inspections and empirical Bayes is discussed. Adaptive sampling strategies are addressed.
Variables Sampling under Measurement Error
Authors: Wilrich Peter‐Th. 
Published: February 2008

Abstract
Single sampling plans for inspection by variables in the case of measurement error are presented that fulfill given requirements on their operating characteristic function. Three cases are considered: (a) measurement standard deviation and process standard deviation known, (b) measurement standard deviation and process standard deviation unknown and each sampled item measured once, and (c) measurement standard deviation and process standard deviation unknown and each sampled item measured more than once.
Attributes Sampling under Classification Error
Authors: Greenberg Betsy S. 
Published: February 2008

Abstract
Inspection, particularly acceptance sampling, is an important aspect of quality control. An acceptance sampling plan is one in which a specified number of units is sampled from each lot, with the lot being accepted if fewer than a fixed number of nonconforming items are found in the sample. However, inspection errors are inevitable. Inspection procedures may commit two types of errors. Items that are classified as nonconforming may be conforming (false positive) and conforming items may be classified as nonconforming (false negative). These errors affect the number of nonconforming items that are identified as well as the outgoing quality obtained from acceptance sampling plans.
Sampling Inspection of Products and Statistical Process Control
Authors: Hryniewicz Olgierd 
Published: February 2008

Abstract
Acceptance control charts, originally proposed by Freund, are the tools of statistical process control (SPC) that combine features of classical control charts and sampling plans used in acceptance sampling of products and services. In contrast with classical Shewhart control charts, their design is based on the requirements typical for acceptance sampling plans by variables. In the article, we present the design principles of the acceptance control charts. The similarity between acceptance control charts and acceptance sampling systems shows that the latter might be used for the purpose of SPC.
Sampling in Semiconductor Manufacturing
Authors: Barlović Robert , Holfeld Andre , Räbiger Jan 
Published: February 2008

Abstract
The task of sampling in state‐of‐the‐art semiconductor manufacturing is to provide relevant metrology data for a representative part of the population in order to monitor and maintain the process, thereby minimizing the risk of processing errors/faults. The decision process itself is restricted by certain expense criteria. Thus, sampling has to select the appropriate material for metrology based on manufacturing needs while fulfilling manufacturing constraints.
Sampling in Data Mining
Authors: Weiß Christian H. 
Published: February 2008

Abstract
After a brief review of basic terms and concepts of knowledge discovery in databases (KDD) and data mining, this article investigates aspects of sampling in data mining. A general scheme of sampling, and particular techniques used in data mining are discussed. The main objective of this article is to provide an overview on areas of KDD and data mining, where sampling approaches are applied.
Internal and External Quality Measures
Authors: Ograjenšek Irena 
Published: February 2008

AbstractThere are two distinct general approaches to the use of statistical methods in quality improvement. These are theproduction approachand themarketing approach. The production approach focuses on quality improvement from the management (internal) point of view using the statistical quality control (SQC) tools and producing predominantly internal quality measures. The marketing approach builds on the customer (external) assessment of quality using tools of survey research, producing predominantly external quality measures. One of the biggest challenges that companies face is a meaningful integration of these two groups of measures with the goal of facilitating continuous quality improvement.
Design and Testing of Questionnaires
Authors: Ograjenšek Irena 
Published: February 2008

Abstract

The questionnaire is the most frequently used data collection tool of survey research. It can be applied in personal, telephone, mail, and on‐line surveys. Two factors which determine the questionnaire design requirements to the largest extent are: the form of the questionnaire (paper
versus
electronic), and the mode of questionnaire administration (interviewer‐
versus
self‐administered questionnaire).

In order to test and evaluate a questionnaire, a wide range of methods can be used. Some (e.g., an expert panel or peer evaluation) are more applicable in the early stages of questionnaire design, other (e.g., a pilot study) should be used prior to the broad questionnaire distribution.
Survey Error
Authors: Reiter Jerome P. 
Published: February 2008

Abstract
In survey samples, the primary objective usually is to estimate unknown population quantities, such as population means or totals. The estimates invariably do not exactly equal the population values, i.e. there are survey errors. This article reviews the main sources of survey errors and general approaches to reducing them.

Survey errors can be classified in two categories,
nonsampling
and
sampling
errors. Common sources of nonsampling error include measurement error, coverage error, mode effect error, nonresponse error, and process error. Measurement error occurs when respondents misinterpret, cannot answer, or inaccurately answer questions. Coverage error occurs when the list used to take the sample does not correspond to all units in the target population. Mode effect error occurs when the way the data are collected influences responses. Nonresponse error occurs when those who do not respond to a survey differ from those who respond to it. Process error occurs when edits, recodes, or other data processing steps go awry. Nonsampling errors are hard to detect and quantify. Their impact can be reduced by careful survey design. Sampling errors occur because only subsets of populations are measured, and estimates vary by sample. Sampling errors arise in every survey, but they are quantifiable.
SERVQUAL
Surveys
Authors: Ograjenšek Irena 
Published: February 2008

Abstract
Service quality is often analyzed using rating scales to assess individual service attributes. The most widely used and most carefully scrutinized rating scale is SERVQUAL. The scale is a result of a systematic ongoing study of service quality that begun in 1983. The model defines quality as the difference between customer perceptions and expectations with regard to quality of delivered service. The respondents are asked to answer two sets of questions dealing with the same subject, one set at a general level, and one for a company in question. Respondents choose from a modified Likert scale which measures intensity of agreement or disagreement with any given statement. Response options range from “strongly agree” and lesser levels of agreement to “neutral” (neither agree nor disagree) and lesser levels of disagreement to “strongly disagree”. Although SERVQUAL has been widely used in business‐to‐business and business‐to‐customer settings, this does not mean the scale has not been subjected to reexamination and criticism. There are several well researched and documented objections to SERVQUAL. As a consequence of these critique, various alternative formulations of the service quality model have been suggested. The only one that, to date, seems to outperfom SERVQUAL is SERVPERF, the performance‐only measure of service quality.
Autocorrelation Function
Authors: Shore Haim 
Published: February 2008

Abstract

Assume that observations in a time series had been taken in fixed time intervals. In most cases (like for stationary time series), pairs of observations separated by a fixed number of intervals,
k
, will display a typical autocorrelation. This correlation is named
autocorrelation of lag k
, denoted as ρ

k

. The autocorrelation function displays ρ

k

as a function of
k
. It plays a central role in time‐series modeling. In this article, the autocorrelation function and the partial autocorrelation function are defined and their estimation procedures detailed.
Homogeneity of Variances
Authors: Koning Alex J. 
Published: February 2008

Abstract
The lack of homoscedasticity affects the validity of statistical tests, and has a negative effect on the coverage of confidence intervals and on the accuracy of estimators. Formal diagnostic tests and diagnostic plots are available for analysis of variance, the linear regression model and the nonparametric regression model. Box–Cox transformations are used to reduce the lack of homoscedasticity.