bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk and the Media
Authors: Mistry Bhavnita , Driedger S. Michelle 
Published: September 2014

Abstract
The news media has long been recognized as an important social player in civil society in shaping public discourse concerning current events, particularly in the communication of science, policy, and risk issues. While the media may not tell us what our thoughts should be, it has considerable power in influencing the same by virtue of what it does, and does not, report. This article explores how the media report and frame risk issues.
Conflicts, Choices, and Solutions: Informing Risky Choices
Authors: Ricci Paolo F. 
Published: September 2014

AbstractThis article deals with key aspects of informing decision making of what may be the optimal choice, given a set of potential choices, unpredictable events, the value of the consequences (positive or negative) associated with each choice, and criteria for making the choice. This article introduces some of those criteria, methods and measures that can be used to inform policy maker relative to the set of options included in the analysis, and provides examples.
Simulation of the Beta‐Stacy Process
Authors: Blasi Pierpaolo 
Published: September 2014

AbstractThis article reviews simulation methods for the beta‐Stacy process, which belongs to the family of neutral‐to‐the‐right (NTR) processes used in Bayesian nonparametric inference on survival distributions. The definition and properties of the NTR processes are recalled, with a view to represent the continuous component of the process at the integrated hazard levelviathe Lévy measure. The conditional distribution of the prior process, given right‐censored data, is described; this represents the key ingredient for devising sampling schemes. Four methods aiming at the generation of increments or trajectories of the hazard rate process are revised. We explain the ideas behind each of them and sketch the correspondent algorithms for the posterior beta‐Stacy process.
Quadrature and Numerical Integration
Authors: Ausín M. Concepción 
Published: September 2014

Abstract
The objective in numerical integration is the approximation of a definite integral using numerical techniques. There is a large number of numerical integration methods in the literature and this article overviews some of the most common ones, namely, the Newton–Cotes formulas, including the trapezoidal and Simpson's rules, and the Gaussian quadrature. Different procedures are compared and illustrated with examples. Discussions about more advanced numerical integration procedures are also included.
M
arkov Chain
M
onte
C
arlo, Introduction
Authors: Wiper Michael P. 
Published: September 2014

Abstract

In this article, we introduce the basic ideas of Markov chain Monte Carlo simulation. We start by briefly commenting on the main ideas of Monte Carlo sampling and then by reviewing the theory of Markov chains, concentrating in particular on the conditions required for the existence of a stationary distribution. We then introduce the Metropolis–Hastings algorithm and show that this generates a Markov chain with a specific stationary distribution. We next introduce some specific ways of implementing the Metropolis–Hastings algorithm,
via
the independence sampler, the Metropolis sampler and the Gibbs sampler among others. Finally, we comment on how the convergence of a Markov chain to equilibrium can be assessed in practice and we finish with an example.
Decision Search via Simulation
Authors: De Iorio Maria 
Published: September 2014

AbstractThis entry provides an overview of simulation‐based methods in decision theory and optimal design problems. We consider decision problems defined by a utility function and an underlying probability model for all unknowns. The search for the optimal design requires the maximization of the expected utility with respect to some design parameter, where the expectation is taken with respect to all the unknowns. In many applications the solution to the optimal design problem is not analytically tractable, as neither the maximization nor the integration have a closed‐form solution requiring approximation and/or simulation‐based methods. We present four related simulation‐based strategies: prior simulations, curve fitting of Monte Carlo experiments, Markov chain Monte Carlo simulations in an augmented probability model, and inhomogeneous Markov chain simulations.
Descriptive Statistics
Authors: Shore Haim 
Published: September 2014

Abstract
Descriptive statistics, calculated from the data, provide summary information about properties of a data set. In this article some of the most widely used descriptive statistics are detailed with the allied formulae. These include the sample average, median and other sample quantiles, mode, standard deviation, coefficient of variation, range, interquartile range, skewness, and kurtosis.
Counts, Rates, and Proportions, Statistical Methods for
Authors: Thyregod Poul 
Published: September 2014

Abstract
Poisson distribution and multinomial distribution are natural distribution models for count and frequency data for categorical variables. When there are only two categories, the multinomial reduces to the binomial distribution.
The distributions are skewed with a variance that changes with the mean value, and therefore standard methods for normally distributed variables such as ANOVA (analysis of variance) and the general linear model cannot reasonably be applied.
When events are counted and classified by two or more categorical variables or when sample units are classified, the counts or frequencies are often presented in tabular form in so‐called contingency tables.
A general framework for analyzing association in multidimensional contingency tables is provided by the log‐linear models. When a set of nested models is formulated, higher‐order interaction terms may be removed successively in analogy with ANOVA models for normally distributed variables. A subset of these models, the logit models, may be used for assessing the effect of explanatory variables on a categorical response variable.
Another important subset of the log‐linear models is constituted by the graphical models. These models allow for a factorization of the joint probability into a product of conditional distributions.
The log‐linear and logit models are special cases of a wider class of models, the generalized linear models. In this framework, the effect of continuous explanatory variables may be modeled in regression‐like models. An example is the logistic regression where the effect of explanatory variables on a response probability is modeled.
When the assumption of independence is violated, overdispersion might result. A method for modeling overdispersion is also presented.
Measures of Association
Authors: Hershberger Scott L. , Fisher Dennis G. 
Published: September 2014

Abstract
Measures of association are numerical estimates of the magnitude of the statistical dependence between two variables, especially if these variables are categorical. A multitude of such measures have been proposed; we review some of the most commonly used ones, dividing them into two types. The first is suitable for nominal scale data, and includes measures of predictive association. The second type is suitable for ordinal data, and includes measures of concordance‐discordance.
Estimation: General Aspects
Authors: Van De Geer Sara A. 
Published: September 2014

Abstract

This paper is concerned with the relationship between sample data and the probability distribution (
P
), which is supposed to have generated it. The estimation of
P
using an empirically derived distribution is discussed along with specific estimators such as the sample mean, the sample median, and a histogram. Estimation by the method of moments and minimum χ
2
estimation are explained, as are the concepts of bias, mean square error, and sufficiency.
Causality
Authors: Evandt Øystein 
Published: September 2014

AbstractDifferent aspects of the causality concept are discussed, inclusive of the fact that no widely accepted definition of causality exists. Still the concept has been essential to the development of science. Further, aspects of how to establish causal relationships are discussed, including causality and statistics, empiricalversusmechanistic causal models, agreement of causal relationships within acceptable margins of error when statistical data are part of establishing such relationships, and Karl Popper's concept of falsifiability. A few concepts and tools related to causality in quality and reliability are briefly mentioned. Newer developments in causality and statistics are mentioned and references to important literature on these developments are given, in addition to references to more established knowledge.
Statistical Efficiency
Authors: Kenett Ron S. 
Published: September 2014

Abstract
The idea of adding a practical perspective to the mathematical definition of statistical efficiency is based on a suggestion by Churchill Eisenhart, who, in a 1978 informal “Beer and Statistics” seminar in Madison, Wisconsin, proposed a new definition of statistical efficiency. Later, Bruce Hoadley from Bell Laboratories, picked up where Eisenhart left off and added his version nicknamed “Vador”. Blan Godfrey, former CEO of the Juran Institute used this concept in his 1988 Youden Address on “Statistics, Quality, and the Bottom Line”, at the Fall Technical Conference of the American Society for Quality Control. Later, Kenett, Coleman and Stewardson in 2003 further expanded this idea adding an additional component, the value of the data actually collected. The concept of practical statistical efficiency (PSE) derived from these developments is introduced below.
Factor Relationship Diagrams
Authors: Hild Cheryl , Sanders Doug 
Published: September 2014

Abstract
Design of Experiments consists of methodologies and data acquisition plans where specific product or process factors (X's) are manipulated across certain levels in order to understand their impact on critical performance parameters (Y's). However, experimental results are impacted by the choice of the design factors (X's), the design structure, and by decisions on how background and noise variables are managed during the experiment. The Factor Relationship Diagram (FRD) is presented as a visual tool effective in assisting in the planning of DOEs, the evaluation of alternative design plans, and in the interpretation of DOE results. The FRD also provides a visual means of describing the error structure and understanding the implications of restrictions on randomization in a DOE.
Data Mining in Quality and Reliability
Authors: Allen Elaine , Seaman Chris 
Published: September 2014

Abstract
Data mining techniques can be separated into two primary areas: those using supervised algorithms and those using unsupervised algorithms. Supervised data mining techniques most closely resemble standard statistical modeling because they are predictive, while unsupervised data mining techniques are associative and are used to examine relationships between variables and may or may not have an implied direction in the relationship. This article uses a large healthcare trial in which data mining techniques provided the only way to analyze the dataset for data quality.
Discrete‐Event Simulation for Reliability Prediction
Authors: Jones Jeff 
Published: September 2014

Abstract
Discrete‐event simulation is an empirical approach to system modeling that can allow us to build real‐world models and attempt to use them to predict what is likely to happen to the reliability of a system in the future. The underlying techniques used in this process involve random sampling from failure distributions, and representation of system structure using such techniques as Petri nets, Markov diagrams, and fault trees. These techniques allow us to build fairly realistic models of complex systems that can be used to understand their dependability behavior under various operating conditions.
Smoothed Function Estimation for Censored Data
Authors: Chaubey Yogendra P. , Sen Arusharka , Sen Pranab K. 
Published: September 2014

AbstractWe review the well‐known smoothing techniques for density and hazard function estimation under random censoring, developed in the recent past. We then outline how the recently proposed smoothing technique by Chaubey and Sen for uncensored data can be adapted to the case of random censoring.
Splines and other Metamodels in Reliability Analysis
Authors: Schueremans Luc , Gemert Dionys 
Published: September 2014

Abstract
Simulation‐based concepts are very attractive to be used in structural reliability because of their inherent simplicity. The major drawback, however, remains the large number of simulations required to obtain sufficiently accurate results. In case of large and complex structural systems with low failure probabilities, the processing time might become unrealistic. To cover this disadvantage, reliability analyses are based on simulation methods (Monte Carlo or directional sampling) in combination with an adaptive metamodel. Often use is made of a low‐order polynomial response surface. The use of splines, neural networks, or ordinary kriging may increase the overall performance.
Failure Modes and Effects Analysis
Authors: Snee Ronald D. , F. Rodebaugh William 
Published: September 2014

Abstract
This chapter gives an overview of failure modes and effects analysis (FMEA) and shows how it is allied in practice by discussing its use in improving manufacturing equipment performance. FMEA is an effective tool for the design of new products and processes and the improvement of existing processes. Using FMEA one can determine how a product, process, or system might fail and the likely effects of particular modes of failure. Having originated in the U.S. military in the 1940s, FMEA today is an analytical tool widely employed in approaches to quality and process improvement such as ISO 9000, ISO/TS 16949, Lean Six Sigma, and Design for Six Sigma.
Aging and Positive Dependence
Authors: Spizzichino Fabio 
Published: September 2014

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.
System Signatures
Authors: Samaniego Francisco J. 
Published: September 2014

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.
Empirical
B
ayes Estimation of Reliability
Authors: Pensky Marianna 
Published: September 2014

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.
Degradation Processes
Authors: Lehmann Axel 
Published: September 2014

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.
Optimal Reliability Design‐Modeling
Authors: Kuo Way , Wan Rui 
Published: September 2014

Abstract
Reliability has become a greater concern in recent years because high‐tech industrial processes with ever increasing levels of sophistication comprise most engineering systems today. To keep pace with this rapidly developing field, this chapter provides a broad overview of recent research on reliability optimization problems. In particular, we address issues related to:

UGF‐based optimal multistate system (MSS) design;

percentile life employed as a system performance measure;

multiobjective optimization of reliability systems, especially with uncertain component reliability estimations;

innovation and improvement in traditional reliability optimization problems, such as fault‐tolerance mechanism and cold‐standby redundancy involved system design.

The chapter concludes with a discussion of future challenges related to reliability optimization.
Sample Size Determination in Hypothesis Testing and Estimation
Authors: Shore Haim 
Published: September 2014

Abstract
Sample data may be collected with different objectives in different scenarios. In some cases, one may wish to collect enough observations that would guarantee minimal prespecified reliability for the conclusions derived from the data. On other occasions, in addition to specified constraints regarding reliability, an optimal value is desired of some objective function that would guarantee that the sampling procedure is optimal in some sense. In all these cases, a major decision variable is the required sample size. In this article, sample‐size determination is addressed, relating, in particular, to four often‐encountered scenarios: hypothesis testing, parameter estimation, relational modeling, and optimal sampling.
Healthcare Performance Reports
Authors: Bevan Richard G. 
Published: September 2014

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.
Statistical Issues in Vaccine Safety Evaluation
Authors: Farrington C. Paddy , Whitaker Heather J. , Hocine Mounia N. 
Published: September 2014

Abstract
This article describes some of the main statistical methods used to evaluate the safety of vaccines. The methods are illustrated using examples of public health concerns that arose following claims of associations, both real and unproven, between vaccination and adverse events. Prelicensure evaluation, surveillance methods, and observational study designs including case‐control, cohort, and case‐only methods are discussed.
Quality in Critical Care Medicine
Authors: Segal Eran 
Published: September 2014

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.
Statistical Process Control in Clinical Medicine
Authors: Winkel Per , Zhang Nien Fan 
Published: September 2014

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.