bluuebunny/crossref_metadata_2025_split huggingface dataset:
Platinum Anticancer Drugs, Chemical Biology of
Authors: Zhang Jenny , Whan Renee , Hambley Trevor 
Published: February 2008

AbstractIn the past, studies into the distribution of platinum anti‐cancer agents inside cancer cells were experimentally challenging because of a lack of readily available and sensitive platinum mapping techniques. Recent advancements in the area of platinum mapping in cells have meant that the biologic fate of cisplatin and its derivatives can be studied in several new ways. In particular, electron microscopy, fluorescence microscopy, and synchrotron radiation induced X‐ray emission have made significant contributions in the study of cisplatinin vitro. The applications of these three techniques and the insights they provide into the intracellular distributions and interactions platinum complexes are reviewed here. The strengths and weaknesses of each technique and their potentials for additional use in this field of research have been evaluated.
Signaling Networks in Biology
Authors: Sarkar Anamika , Wist Aislyn , Iyengar Ravi 
Published: February 2008

Abstract
Signal transduction in cells has been an area of intense study for many years. However, until recently, the focus was on interactions between individual components rather than on the global behavior of the cell signaling networks. New experimental technologies, such as protein and DNA microarrays, high‐throughput screening instrumentation, and diverse compound libraries, allow for many interactions to be examined simultaneously. Along with new experimental methods, quantitative models with mathematical methods, such as deterministic, stochastic, and statistical analyses and graph theory, have been useful in the mapping and analysis of intracellular signaling networks. Here we describe current experimental and computational approaches that are used to develop a global understanding of the complex behavior of intracellular signaling networks. We also discuss important applications of signaling network analysis, including the discovery of new drug targets, the identification of the signaling components responsible for the side (off‐target) effects of drugs, and the development of combination therapies.
Aliasing in Fractional Designs
Authors: McGrath Richard N. , Lin Dennis K.J. 
Published: February 2008

Abstract

In this short exposition, we provide an overview of the aliasing (or confounding) among effects that is caused by studying fewer treatment combinations than required in a full factorial design. We show, by example, how to determine the alias structure of the regular two‐level fractional factorial (2

k
−
p

) designs, nonregular two‐level designs, and the regular three‐level fractional factorial (3

k
−
p

) designs.
Blocking
Authors: Gilmour Steven G. 
Published: February 2008

Abstract

When an experiment is designed,
blocking
involves grouping the runs together in sets which are expected to give similar responses. It is used to improve the estimation of the treatment effects of interest. Complete blocks can be used if the number of runs in each block is equal to the number of treatments. Otherwise incomplete blocks are needed. Different methods of choosing incomplete blocks are used depending on whether the treatments are simple, discrete categories or combinations of levels of several factors. Extensions include resolved block designs and row–column designs, such as Latin squares.
Factorial Experiments
Authors: Kenett Ron S. , Steinberg David M. 
Published: February 2008

Abstract
Proactive experiments are an essential element of the learning process, in all application areas. Statistically designed experiments have been used to accelerate learning since their introduction by R.A. Fisher in the first half of the twentieth century. In this article we review factorial and fractional experiments and provide some examples from the chemical and semiconductors industries marketing and computer intensive simulations, and and briefly discuss the role of factorial experiments in statistical education.
Fractional Factorial Designs
Authors: Voelkel Joseph G. 
Published: February 2008

Abstract

If only a fraction of all the treatment combinations of a full factorial design are selected to be run, the resulting design is called a
fractional factorial design
. By carefully deciding which fraction of the treatment combinations to run, it is possible to construct fractional factorial designs with good properties. We examine these designs in detail for designs in which each factor is studied at two levels. We show how to construct these designs and how to find their properties. We discuss some uses of these designs as well as their advantages and disadvantages. An example is given of a design and its analysis. Extensions are considered to other situations, including three‐level designs, blocked designs, and split‐plot designs.
Interactions
Authors: Reese C. Shane 
Published: February 2008

AbstractStatistical approaches for assessing the effect of a set of explanatory variables on a response variable often involve estimation of joint or interaction effects. This article provides a historical perspective on interaction effects from both an effects model and a cell means model perspective. Factorial designs are presented as an effective means of conducting studies where interaction effects are estimable. The assessment of presence or absence of interaction effects is demonstrated with both statistical hypothesis testing and graphical techniques. An example from the commercial automotive industry provides an illustration of interaction effects.
L
enth's Method for the Analysis of Unreplicated Experiments
Authors: Lenth Russell V. 
Published: February 2008

Abstract
Lenth's method is an objective method for deciding which effects are active in the analysis of unreplicated experiments, when the model is saturated and hence there are no degrees of freedom for estimating the error variance. The article reviews its formulation, computation, and examples, and discusses the choice of critical values and the presentation and interpretation of the results.
P
lackett–
B
urman Designs
Authors: Tyssedal John 
Published: February 2008

Abstract
Plackett–Burman designs are important screening designs. In this article we explain how they can be constructed and how they can be classified according to projectivity of two‐level designs. We give a reasonably exhaustive list of existing methods for analyzing them and illustrate one of these methods by an example. Some techniques useful for constructing designs with good projective properties for various experimental situations are described at the end.
Factorial Designs, Resolution of
Authors: Romano Daniele 
Published: February 2008

AbstractAfter providing the definition of resolution for 2k−pdesigns we present two rationales that justify the practical importance of resolution as a criterion for selecting good fractional designs. Insight is given into designs of resolution III, IV, and V, i.e., the most widespread in practice. Then we describe how to determine the resolution of a design and a simple rule for finding half‐fractions of maximum resolution. Smaller fractions of maximum resolution can be found in specialized books referenced in the text. We also describe some circumstances when the resolution criterion needs to be supplemented or replaced by other user‐relevant criteria.
Sample‐Size Determination in Experimental Designs
Authors: Lenth Russell V. 
Published: February 2008

Abstract
Sample‐size determination is a procedure by which we decide how much data are required in order to achieve stated goals for a statistical study. The article focuses primarily on the two most popular paradigms (confidence interval width and power), and on the use of software to solve sample‐size problems. It discusses what situations are most pertinent with regard to sample‐size planning, the importance of eliciting a relevant effect size, and the use of pilot data. An illustration is given for a complex experiment. There is also information on the related issue of sample‐size allocation, and on sample‐size requirements for pilot studies.
Split‐Plot Designs
Authors: Goos Peter 
Published: February 2008

Abstract

Split‐plot designs are
nested designs
that are frequently used in industrial experimentation and result in correlated observations. A correct analysis of the data from split‐plot designs takes account of the correlation. However, as split‐plot designs are often used inadvertently, the typical split‐plot correlation structure is not recognized in many applications. The resulting statistical analysis is then inappropriate. In this contribution, the agricultural origin of split‐plot experimentation is discussed first. Design aspects are discussed as well as the classical analysis of variance (ANOVA) (
see
Analysis of Variance
) and industrial applications of split‐plot designs. It is explained that a generalized least‐squares analysis can be used for estimation and inference and different approaches to constructing split‐plot designs are discussed.
Central Composite Designs
Authors: Draper Norman R. 
Published: February 2008

AbstractChoosing a suitable experimental design is an important first step in investigations of the factors that may influence one or more response variables. Such investigations often lead to the representation of the response variable as a second‐order (quadratic) surface in the predictor variables. Central composite‐type designs are popular, classical, and excellent choices for this purpose. A portion of a design of this type can be performed initially to fit a first‐order model. A second‐order portion can then be added, if it becomes clear that the first‐order fit suggests the need for supplementary information. A three‐predictor variable data set is worked through to illustrate the decisions involved in these types of studies.
Mixture Experiments
Authors: Piepel Greg F. 
Published: February 2008

AbstractAmixture experimentinvolves combining two or more components in various proportions or amounts and then measuring one or more responses for the resulting end products. Other factors that affect the response(s), such as process variables and/or the total amount of the mixture, may also be studied in the experiment. Amixture experiment designspecifies the combinations of mixture components and other experimental factors (if any) to be studied and the response variable(s) to be measured.Mixture experiment data analysesare then used to achieve the desired goals, which may include (a) understanding the effects of components and other factors on the response(s), (b) identifying components and other factors with significant and nonsignificant effects on the response(s), (c) developing models for predicting the response(s) as functions of the mixture components and any other factors, and (d) developing end products with desired values and uncertainties of the response(s).Given a mixture experiment problem, a practitioner must consider the possible approaches for designing the experiment and analyzing the data, and then select the approach best suited to the problem. Eight possible approaches are (a) component proportions, (b) mathematically independent variables, (c) slack variable, (d) mixture amount, (e) component amounts, (f) mixture‐process variable, (g) mixture of mixtures, and (h) multifactor mixture. The article provides an overview of the mixture experiment designs, models, and data analyses for these approaches.
Response Surface Methodology
Authors: Steinberg David M. , Kenett Ron S. 
Published: February 2008

AbstractResponse surface methodology is a general strategy for combining designed experiments and regression analysis to explore the relationship between one or more response variables and a set of factors that are thought to affect the responses. Some of the key elements in this strategy include sequential progress, a matching of experimental designs to the complexity of needed regression models and rapid progress to the most interesting parts of the factor space. These methods are often used to seek optimal settings of the factors for reaching specific values of the response, such as a maximum or minimum value. Response surface methods are widely used in research and development and industrial applications.
Rotatable Designs and Rotatability
Authors: Draper Norman R. 
Published: February 2008

Abstract

Choosing a suitable experimental design is an important step in the investigations of the factors that may influence one or more response variables. There are several criteria to keep in mind when the choice is made, one of which is
rotatability
. When a design is
rotatable
, it will produce information that predicts ŷ with the same precision at all points equidistant from the coded origin of the design. Another way of saying this is that the contours of
V
(ŷ), the variance of the predicted value, will be spherical about the design origin. In experimental practice,
exact
rotatability is not essential, but the knowledge of how to obtain it is useful in producing approximate rotatability while perhaps attaining other desirable design characteristics.
Dispersion Effects
Authors: Tort‐Martorell Xavier , Marco Lluis 
Published: February 2008

Abstract
Reduction of variation is a crucial ingredient for quality and productivity improvement. An economical and effective approach is to conduct statistically designed experiments, not only to identify controllable factors that affect the mean level (location) of the response, but also to identify and learn about the ones that affect the dispersion. This article explains how control factors can affect the response variability, how they can be identified and estimated, and gives two well‐documented examples that show how this knowledge can be used to improve products and processes.
Performance Measures for Robust Design
Authors: Murphy Terrence E. , Tsui Kwok‐Leung 
Published: February 2008

Abstract

Despite their widespread use as performance measures in robust design, Taguchi's signal‐to‐noise ratios (SNRs) do not always minimize quadratic loss. The relationship between minimization of expected quadratic loss and Taguchi's SNR was articulated by León
et al.
1. Their performance measure independent of adjustment (PerMIA) is a general performance measure which justifies the use of the Taguchi two‐step procedures as a method of minimizing quadratic loss under certain models. We review the basic conceptual framework of the PerMIA and the literature related to this performance measure.
Signal–Response Systems
Authors: Miller Arden 
Published: February 2008

Abstract
The defining characteristic of a signal–response system is that the function of the system requires a response variable to take different values when the value of a signal factor changes. The performance of the system depends on the relationship between the signal and the response. Improving signal–response systems using designed experiments is usually approached as a special case of parameter design. This article describes several types of signal–response systems, discusses how their performance depends on the signal–response relationship and presents methodology for analyzing experiments aimed at improving their performance.
B
ergman–
H
ynén Method for Dispersion Effects
Authors: Arvidsson Martin , Bergman Bo 
Published: February 2008

Abstract
This paper presents the Bergman‐Hynén method for dispersion effect identification in unreplicated fractional factorials. In contrast with other methods the Bergman‐Hynén test statistic has a known reference distribution which allows use of formal hypothesis testing for the dispersion effect identification.
B
ox–
M
eyer Method for Dispersion Effects
Authors: Meyer R. Daniel 
Published: February 2008

Abstract

One aspect of product quality is variability in finished product. Taguchi showed the world that variability is a consequence of product design and not just manufacturing performance. He also showed us that variability can be studied in engineering optimization experiments. Product variables that affect variability are said to have dispersion effects (
see
Dispersion Effects
). Using the Box‐Meyer method, dispersion effects can be estimated simply from the same unreplicated two‐level designs used to study effects on the mean performance.
Ridge Analysis in Experimental Design
Authors: Draper Norman R. 
Published: February 2008

Abstract

Ridge analysis is a valuable tool for exploring second‐order surfaces that have been fitted to experimental data. Paths of absolute maxima (ridge of maxima), absolute minima (ridge of minima), or of intermediate turning values (ridges that are neither overall maxima nor minima but are best locally) can be located and followed. The paths can start from
any
selected location in the predictor space; often the coded origin of the experimental space would be chosen.
L
évy Processes
Authors: Kella Offer 
Published: February 2008

Abstract
This entry contains an overview of Lévy processes and their multidimensional extensions, which are processes having stationary, independent increments that are continuous in probability thereby having versions with almost surely right continuous sample paths. Special cases are Poisson processes, Brownian motions and compound Poisson processes. Infinite divisibility, which is closely related to Lévy processes, is defined and discussed as well as the reflected version of these processes and associated martingales, both in the one‐ and multidimensional cases. Markov additive processes, which are extensions of Lévy process, as well as their reflected versions and associated martingales are introduced as well.
Availability Models
Authors: Aven Terje 
Published: February 2008

AbstractConsider a system which is repaired or replaced at failures. The problem is to analyze the availability of the system at a fixed point in time or for an interval [u,v]. This article presents models and methods for analyzing the availability, both for one‐unit systems and for more complex systems comprising several components (monotone systems). Exact formulae as well as approximation approaches based on asymptotic results are covered.
Degradation and Failure
Authors: Nikulin Mikhail 
Published: February 2008

Abstract
Degradation and failure time data are usually used in reliability, bioreliability, biology, and medical statistics to estimate the survival or the intensity functions. The degradation data are often a rich source of reliability information and offer many advantages over failure time data, when the aging process due to cumulated wear and tiredness is observed. Some statistical approaches to model the relationship between degradation and failure data are considered here.
Competing Risks
Authors: Lindqvist Bo Henry 
Published: February 2008

AbstractConsider a unit which can experience any one ofkcompeting failure types, and suppose that for each unit we observe the time to failure,T, and the type of failure,C ∈ {1, 2, …,k}. The case of observing the pair (T,C) is termedcompeting risksin statistical literature. After considering some examples we review basic notation and theory of competing risks. In particular we consider the latent failure time approach to competing risks in which thekrisks are represented by potential failure timesT1, …,Tkwhere only the smallest,T = minjTj, is observed together with its indexC = arg minjTj. In reliability studies, the marginal distributions of theTjare often of primary interest, but are unfortunately nonidentifiable in general. Additional, though nontestable, assumptions to obtain identifiability are considered, as are bounds for the marginal distributions given in terms of observable functions. The likelihood function of right censored competing risks data is given and its consequences for both parametric and nonparametric estimation are explained. Extensions of the classical theory of competing risks to more general Markov models and to repairable systems are briefly discussed.
Subjective Life Models
Authors: Mendel Max 
Published: February 2008

Abstract
This chapter deals with several subjective life models that are derived from first principles. First, the ingredients of these models including the hazard and its differential, maintenance policies, aging rates, and failure rates are rigorously defined from a decision‐theoretic perspective. Then the concepts of no‐aging and aging are defined and the corresponding statistical models are derived. Also, the circumstances under which these models converge to form commonly used statistical models are shown.
Precedence Tests
Authors: Balakrishnan Narayanaswamy , Tony Ng Hon Keung 
Published: February 2008

Abstract
A nonparametric two‐sample life test based on the order of early Failures–precedence test–is discussed. First, the basic idea of precedence‐type life‐testing procedures and some related works in the literature are reviewed. Then, some efficient alternatives for the precedence test are discussed which include the maximal precedence test, Wilcoxon‐type rank‐sum precedence tests, and the weighted precedence and maximal precedence tests with an extension to progressively censored data. Exact powers of these test procedures under the Lehmann alternative are discussed and evaluated numerically. Finally, applications of precedence‐type statistics to multisample selection problems are briefly mentioned.
Change‐Point Models
Authors: Jensen Uwe , Lütkebohmert Constanze 
Published: February 2008

Abstract
Change‐point models describe formally the problem of deciding whether a stochastic process is homogeneous in some sense. We review and classify various change‐point models with a focus on the more recent ones. In particular, we consider change‐point detection models in which the decision is based on sequentially observed data. Both, discrete and continuous models are presented. Finally, we give an overview of some parametric and nonparametric regression and hazard rate models with change points.
Statistical Process Control,Bayesian
Authors: Tsiamyrtzis Panagiotis , Hawkins Douglas M. 
Published: February 2008

AbstractStatistical process control (SPC) is a collection of statistical methods which can be used to maintain quality of an outcome of a process. The SPC tools originally developed within a purely frequentist framework required independent identically distributed observations and known process parameters. Before actual process monitoring can begin (phase II) they require a phase I data gathering exercise for chart calibration. The Bayesian SPC approach avoids the need for a large phase I calibration exercise, and provides a consistent well defined framework for use of prior information which is almost always available but is hard to accommodate and so is ignored by the traditional frequentist SPC methods. Furthermore, in the Bayesian SPC we can transform the inferential procedure to an equivalent decision theory problem. The ability to provide (relatively easy) forecasting is another great advantage of the Bayesian perspective. Although Bayesian methods may require substantially more computing effort than classical control charts, their benefits increasingly outweigh this cost in the modern setting of accessible computer power.
Bayesian Design of Reliability Experiments
Authors: Meeker William Q. , Zhang Yao 
Published: February 2008

AbstractThis article describes Bayesian methods for planning reliability experiments. A Bayesian reliability experiment is a reliability experiment in which the experimenters intend to use prior information and for which Bayesian methods will be used in making inferences from the resulting data. Within the framework of a general model for reliability involving unknown parameters, we describe the basic ideas of Bayesian experimental design. We then describe a large‐sample normal approximation that provides an easy to interpret yet useful simplification to the problem of Bayesian evaluation of reliability experiments and discuss aspects of finding optimum experiments within this framework. We point to several examples of the methodology that are available in the statistical literature.