bluuebunny/crossref_metadata_2025_split huggingface dataset:
Half‐Normal Plot
Authors: Taam Winson 
Published: February 2008

Abstract
A half‐normal plot is a graphical tool to discern active contributors from inert ones in a factorial experiment. It is designed for two‐level factorial experiments but can be adapted for experiments with other levels. Plotting absolute effect estimates and their corresponding half‐normal scores provides a graphical display that compares the relative strength of various effects in the experiment. Its ease of use, visual appeal, and simplicity are reasons for its popularity.
Block Designs, Incomplete
Authors: Prescott Philip 
Published: February 2008

AbstractIn experimental situations, incomplete blocks occur when there is insufficient homogeneous material within a block for all treatments to be applied. Balanced incomplete block designs are such that all treatments are equally represented within the design and every pair of treatments occurs together within a block the same number of times. We give examples of unreduced and reduced balanced incomplete block designs, and briefly discuss several methods of constructing such designs. The intrablock analysis of variance for the general case is given in detail, and this is extended to the simplified analysis which results when the design is balanced.
Latin Hypercube Designs
Authors: Tang Boxin 
Published: February 2008

AbstractLatin hypercubes are a rich class of designs that are suitable for computer experiments and numerical integration. They are easy to generate and achieve maximum stratification in each of the univariate margins of the design region. This article introduces Latin hypercubes, explains how they can be used in computer experiments and numerical integration, and discusses their strengths and weaknesses. A design may not perform well in terms of other criteria such as those of space filling and orthogonality, simply because it is a Latin hypercube. The article concludes with a discussion on some of the methods for constructing Latin hypercubes that have better space‐filling or orthogonality properties.
Latin Squares and Related Experimental Designs
Authors: Morgan J. P. 
Published: February 2008

AbstractLatin squares and their generalizations are explored for their utility as experimental designs. The popularity of Latin square designs is due to their parsimonious use of experimental material while providing orthogonal estimation of treatment factors. As a design with twov‐level blocking factors, av × vLatin square can evaluate levels of onev‐level treatment factor orthogonally to both of the blocking factors while requiring onlyv2experimental units (one for each combination of the blocking factors). Generalizations include: designs with more rows and columns (F‐squares and rectangles, Youden designs), squares with more than one experimental unit per row‐column combination (semi‐Latin squares), and designs based on multiple squares. Mutually orthogonal Latin squares allow more treatment factors to be orthogonally accommodated with the same number of experimental units as a single Latin square. This concept is explored in the wider context of the generalizations listed above.
Main Effect Designs
Authors: Jacroux Mike 
Published: February 2008

Abstract
In this article we consider experimental situations where a screening experiment is to be run and primary interest is in the identification of variables which have an influential effect on the process being studied. In such cases, main effects designs are often used. Main effects designs typically allow for the efficient estimation of main effects parameters and are particularly useful when the initial number of experimental variables being studied is large compared to the number of runs in the screening experiment. Here we describe a number of the orthogonal and nonorthogonal main effects designs available to practitioners along with some of their individual advantages and disadvantages.
Optimal Design
Authors: Atkinson Anthony C. 
Published: February 2008

AbstractThe article focuses on the use of the methods of optimal experimental design to solve a number of nonstandard problems arising in quality and reliability. Examples include response‐surface designs for an arbitrary number of observations. Because some combinations of factors may produce unstable results, designs are found over irregular design regions. The method of dividing such designs into blocks is also shown. Mixture designs, again over constrained regions, are mentioned. The theory underlying the construction of the designs is integrated using a general equivalence theorem which leads to algorithms for the construction of designs and to the assessment of the efficiency of designs. References are given to recent work.
Orthogonal Arrays
Authors: Notz William I. 
Published: February 2008

AbstractIn this article we use a simple example to introduce the notion of orthogonality as a desirable characteristic of an experimental design. In particular, we show that a lack of orthogonality prevents us from distinguishing the effects of some of the factors. We then formally define a class of designs, known asorthogonal arrays, which possess attractive orthogonality properties. Some examples of orthogonal arrays are given. These are followed by comments about the construction of orthogonal arrays and sources where one can find tables of orthogonal arrays. Next, we discuss the use of orthogonal arrays in several applications. These include fractional factorial experiments for controlling aliasing, with special attention to designs in which no main effects are aliased; screening experiments in which orthogonal arrays have special projection properties; response surface modeling in which orthogonal arrays yield optimal designs; robust parameter designs in which orthogonal arrays are the basis for cross arrays; and in computer experiments where orthogonal arrays produce designs that space points evenly on lower‐dimensional subspaces. We conclude with an example.
Projectivity in Experimental Designs
Authors: Tyssedal John 
Published: February 2008

Abstract
Projectivity concerns the properties of a factorial design when it is restricted to a subset of experimental factors. In this article we stress the importance of taking projective properties into account when a screening experiment is performed. We present results that can be used to decide on the projectivity of two‐level designs, show by an example how these properties can be taken advantage of in order to identify active subspaces of factors and give various techniques that can be used to construct designs with good projective properties for various experimental situations.
Screening Designs
Authors: Jacroux Mike 
Published: February 2008

Abstract
In this article, we consider experimental situations where the initial number of predictor variables to be studied in an experimental process is large and it is desired to identify which variables actually have a significant influence on the process being investigated. Screening designs are used primarily for this purpose. Here we describe some of the two‐level screening designs that are commonly used along with some of their individual advantages and disadvantages.
Supersaturated Designs
Authors: Lin Dennis K.J. 
Published: February 2008

AbstractIn the early stages of industrial experimentation, one often has a large set of candidate factors believed to have a possibly significant influence on the response of interest, although it is reasonable to assume that only a small fraction are influential, a condition known aseffect sparsity. A design of an experiment with the property that the number of potential effects exceeds the number of experimental runs (observations) is called asupersaturated design. These designs are useful as screening tools. This article explains what these designs are, why you would want to use them, and what are their pros and cons relative to other designs. Both design and analysis issues will be discussed.
Box–Behnken Designs
Authors: Robinson Timothy J. 
Published: February 2008

AbstractDesigns for second‐order response surface models play an important role in response surface methodologies. The central composite design and Box‐Behnken designs have served as popular choices for designs for second‐order models when design economy and precise prediction variance are desired. The Box‐Behnken design has been widely used in completely randomized experiments, split‐plot experiments, and within the robust parameter design setting. This article provides a historical background for the Box‐Behnken design, describes its construction, and reviews various contributions in the literature regarding the use of these designs.
Sequential Experimentation
Authors: Gunter Bert 
Published: February 2008

Abstract
Sequential experimentation is the application of statistical experimental design methods to improving processes when many experimental factors must be studied. It emphasizes the sequential use of small two‐level designs and steepest ascent to identify critical factors and improved settings, and the sequential assembly of second‐order designs to elucidate the nature of the response surface in the improved operational region when necessary.
Product Array Designs
Authors: Zhu Yu 
Published: February 2008

AbstractThis article is a review of product arrays proposed by Taguchi for robust design experiments. The motivation and advantage of using product arrays in robust design are discussed. Furthermore, alternative modeling approaches such as the location‐dispersion modeling approach and the response modeling approach are introduced. As an extension of product arrays, compound orthogonal arrays are defined. This article also discusses the optimal selection of product arrays.
Robust Design
Authors: Ankenman Bruce E. 
Published: February 2008

AbstractRobust design is a methodology that seeks to set the controllable factors of a system in order to achieve a consistent and desirable response from the system. The defining characteristic of robust design is the emphasis on achieving a consistently desirable response, not just a response that is desirable on average. The robust design procedure involves designing, conducting, and analyzing experiments. In this article, robust design methodology is explored for two typical cases. The first case is when the variability in the system is due to replication error. The second is when there are certain factors, callednoise factors, which can be controlled during an experiment, but will vary randomly when the system is put into its standard operating mode. Models for the response, data analysis techniques, and experimental design techniques will all be discussed and demonstrated through a simple manufacturing example.
Signal‐to‐Noise Ratios for Robust Design
Authors: Murphy Terrence E. , Tsui Kwok‐Leung , McShane‐Vaughn Mary 
Published: February 2008

Abstract

Taguchi introduced a collection of performance measures called
signal‐to‐noise ratios
(SNRs) as a core component of the robust design methods he introduced to US corporations in the 1980s. The SNR is the most controversial part of Taguchi's methods because it summarizes the quality information from the entire experiment in a single entity. While the use of a single performance measure provided a conceptual simplicity attractive to engineers, it also introduced certain statistical controversies. We review the strengths and limitations of this performance measure and the evolution of related research. Our discussion includes the use of the SNR for both static and dynamic robust design (
see
Robust Design
).
Generalized Linear Models
Authors: McGrath Richard N. 
Published: February 2008

AbstractWhen analyzing experimental data, we often use regression or analysis of variance techniques to estimate and perform statistical hypothesis tests about parameters of the model. Under the standard linear regression model, we assume that the error terms in the model follow a normal distribution with constant variance. Unfortunately, many experimental responses are not normally distributed and some accommodation to the model must be made, perhaps by transforming the observations. In generalized linear models, we are not restricted to the normal distribution as the distributional form, we can use any member of the exponential family of distributions. This family includes the binomial and Poisson for count data as well as the gamma distribution and others for continuous data. After providing some background information on generalized linear models, we will discuss application of these models in designed experiments in three situations: with a integer response, when the mean response is of interest, and when both the mean and the variance are to be modeled.
Harvey's Method for Dispersion Effects
Authors: Brenneman William A. 
Published: February 2008

AbstractThere has been considerable interest in the use of unreplicated designed experiments to identify both location and dispersion effects. This article takes a look at the two‐step approach which incorporates a noniterative identification step followed by an iterative final estimation step. This two‐step approach eliminates some of the bias present in the noniterative methods.
Software Reliability Modeling and Analysis
Authors: Xie Min , Yang Bo 
Published: February 2008

Abstract
Computer systems can be found almost everywhere in our daily life and they are also extensively used in areas such as defense and finance. Unlike hardware reliability which is a well‐developed area, software reliability is relatively new and is generally more difficult to insure. In order to improve the reliability of a software product, the software has to be tested extensively prior to its release. Software reliability models are commonly used to monitor the testing process and to measure and predict future reliability of the software. Such study is also useful to determine the release time of the software product. During the past three decades there have been many software reliability models proposed and studied. This chapter reviews some of the models and discusses the applications of the models for decision making in software development.
Stochastic Deterioration
Authors: Van Noortwijk Jan M. , Kallen Maarten‐Jan 
Published: February 2008

Abstract
To ensure that a system or structure performs its required functions (including safety), maintenance should be performed on a regular basis. To optimize maintenance, information about the time to failure and/or the deteriorating condition is needed. Unfortunately, there is often a lot of uncertainty involved in the applied deterioration process. Mathematical models can be used to account for the stochastic nature of deterioration over time. This article reviews two approaches in modeling deterioration: the actuarial approach based on the notion of failure rate and the physical approach based on the notion of failure due to the stress exceeding the strength. Candidates for stochastic deterioration models are finite‐state Markov processes and Markov processes with independent increments (e.g., gamma process, compound Poisson process, and Brownian motion with drift).
Aging and Positive Dependence
Authors: Spizzichino Fabio 
Published: February 2008

AbstractMultivariate survival models for vectors of dependent lifetimes typically manifest also a phenomena of aging. This chapter presents a discussion about the connections existing between these types of properties and point out some aspects that they have in common, from a conceptual point of view. For this purpose some basic notions of positive dependence and some notions of multivariate aging are being recalled.
Stochastic Orders and Aging
Authors: Belzunce Félix , Shaked Moshe 
Published: February 2008

Abstract
Many reliability systems or components of such systems have random lifetimes that indicate aging properties of these systems or components. Most of these aging properties can be characterized by various stochastic orders. Such characterizations are useful for the identification of the corresponding aging properties and for a better understanding of the meaning of these properties. In this article we describe such characterizations.
Characterization in Reliability
Authors: Klebanov Lev B. 
Published: February 2008

Abstract
We describe the problems of characterization of probability distributions arising in reliability theory. These characterizations are mostly based on the property of aging of technical devices or systems. Among them are the classical lack of memory property and its generalizations, functioning properties of redundant systems, relations between the reliabilities of two systems, and reliability of hierarchical structures. Typical results on the characterizations and their stability inside some popular classes of distributions with the special character of the hazard function are also given.
Random Evolutions toward Applications
Authors: Koroliuk Vladimir S. , Limnios Nikolaos 
Published: February 2008

Abstract
This article gives a short and elementary presentation of random evolutions toward applications in reliability and quality engineering. At first, an example concerning the dynamical stochastic system in a two state Markov media is presented. A dynamical system in continuous time is presented since nowadays they are widely used in dynamic reliability modeling. Limit theorems in averaging and diffusion approximation scheme for ergodic and nonergodic media are also presented. Remarks on bibliography on further topics related to random evolutions are given.
Damage Processes
Authors: Singpurwalla Nozer D. 
Published: February 2008

Abstract

The point of view that we adopt here is that damage is an abstract notion that conveys an intuitive import. Specifically, an item fails when its damage exceeds a threshold. Whereas damage cannot be observed and measured, the surrogates that it spawns such as crack growth, CD4 cell counts, and wear, can be. These surrogates are known as
markers
. With the above in mind, we offer here a probabilistic architecture that endeavors to make the notion of damage precise and still retain its intuitive import. Our architecture looks at damage as a cumulative hazard and describes its evolution
via
a nondecreasing stochastic process. The observable markers associated with damage are also modeled by a stochastic process that is cross‐correlated with the damage process. Thus a bivariate stochastic process with one component that is nondecreasing and the other that may fluctuate around some mean could be a suitable model for encapsulating the phenomenon of damage and its markers. The latent nondecreasing process leads to the fluctuating observable processes, and an item fails when the former hits a threshold. The second feature of our architecture pertains to the threshold. We argue that the threshold needs to be random and has an exponential distribution with scale one; we call such a threshold the
hazard potential
of an item. To conclude, our perspective on what constitutes a damage process is starkly different from that which is prevalent in the reliability and the survival analysis literatures. Hopefully, it offers a platform for describing an ill‐defined but much‐discussed phenomenon.
System Signatures
Authors: Samaniego Francisco J. 
Published: February 2008

AbstractThe basics of structural reliability theory for engineered systems are briefly reviewed. Some of the literature on system signatures, defined in the context in which a system's components have independent and identically distributed (i.i.d.) lifetimes, is discussed. In brief, the signature of ann‐component system is a probability vector whoseith element is the probability that the ith component failure is fatal to the system. Representations are given for the survival function, density, and failure rate of systems with i.i.d. component lifetimes in terms of the system's signature vector and the common distribution of its components. The utility of signatures in the comparative study of system performance is illustrated through several “preservation” theorems. The occurrence of signatures in a variety of reliability contexts is noted. These include the study of closure theorems for the IFR class, comparisons between simple and indirect majority systems, system comparisons via stochastic precedence and via the information and the dispersive orderings, hazard rate ordering between independently drawn order statistics, optimization problems in reliability economics, and comparisons between communication networks.
Software Reliability:Bayesian Analysis
Authors: Wiper Michael P. 
Published: February 2008

AbstractIn this article, the use of Bayesian methods in software reliability predictions is reviewed. In particular, the article studies software reliability models for interfailure times or for the numbers of failures in a given time period. After a general introduction to the use of the Bayesian approach to inference in the software reliability field, in particular, commenting on the use of the prequential likelihood for model checking, the article concentrates on reviewing Bayesian analyses of some of the most well known software reliability models, outlining the concatenated failure rate model of Jelinski and Moranda. The article ends with a real data example where the goodness of fit and reliability predictions of various models are compared.
B
ayesian Reliability Analysis
Authors: Martz Harry F. 
Published: February 2008

Abstract
Bayesian reliability methods permit the formal incorporation of pertinent supplementary information about the parameters of interest in a statistical analysis beyond that contained in the sample data. This additional information is contained in the prior distribution of the parameters. Bayes' theorem is used to combine the prior and sampling distributions to form the posterior distribution of the parameters. Then all the desired inferences are obtained from this joint posterior. In most practical applications, Markov chain Monte Carlo sampling techniques are used to numerically perform the required calculations. A real‐world reliability example that illustrates the Bayesian approach is presented.
Empirical
B
ayes Estimation of Reliability
Authors: Pensky Marianna 
Published: February 2008

Abstract
Empirical Bayes decision procedures are decision theoretic procedures that utilize past data as a means for bypassing the necessity of identifying a completely unknown prior distribution. The article reviews the parametric and nonparametric empirical Bayes techniques and their application to estimation of the mean lifetime, the reliability function, and the failure intensity function.
Maintenance andMarkov Decision Models
Authors: Dekker Rommert , Nicolai Robin P. , Kallenberg Lodewijk C.M. 
Published: February 2008

AbstractIn this chapter we first give an introduction to Markov decision theory. We state the main optimality criteria and solution approaches. Next we sketch how it can be applied in maintenance theory. In particular, we deal with the civil infrastructure sector and show what kind of results it brings. Finally we also indicate which problems arise in applications.
Prior Distribution Elicitation
Authors: Quigley John , Bedford Tim , Walls Lesley 
Published: February 2008

Abstract
An overview of key issues associated with the elicitation of a prior probability distribution is provided. The nature of subjective probability is discussed, and the main causes of biased assessments are identified. Evidence is presented suggesting that people can improve their probability assessments under the correct conditions. The ways in which the elicitation of a prior distribution can be embedded within a process to minimize the impact of bias are examined. More advanced issues, namely validation of a prior distribution and reducing the cognitive burden on experts, are described.