bluuebunny/crossref_metadata_2025_split huggingface dataset:
False Discovery Rate
Authors: Abramovich Felix , Benjamini Yoav 
Published: September 2014

Abstract
For the multiple hypotheses testing problem consider the proportion of falsely rejected hypotheses (false discoveries) among the total number of rejections. The expected value of this proportion, called the False Discovery Rate (FDR), is a useful criterion to control as an alternative to the traditional familywise error rate (FWE) that suffers from low power properties when the number of tested hypotheses is large. In a way, controlling FDR is adaptively inbetween ignoring multiplicity altogether and a conservative control of FWE. Several FDR controlling procedures are presented and others are reviewed. Various extensions and applications of the FDR are discussed.
Kurtosis
Authors: Zenga Michele 
Published: September 2014

AbstractThe interpretation of the kurtosis index β2proposed by Karl Pearson is first examined. The Moors' indexTbased on quantiles and some partial orderings of kurtosis (by Van Zwet, Laurence, and Loh) are presented. Zenga's new approach to kurtosis, working for symmetrical as well as for asymmetrical distribution, is introduced. This latter approach is based on the definition of a particular transformation increasing kurtosis; a kurtosis curve, for the left and the right side of a distribution, can thus be obtained.
Generalized Variance
Authors: Gupta Ashis Sen 
Published: September 2014

AbstractGeneralized variance (GV), proposed by Wilks, is an one‐dimensional measure of multidimensional scatter. It plays an important role in both theoretical and applied research. GV has been extended to Standardized GV (SGV) to enable comparison of scatters in differing dimensions. Interesting results on the distribution, estimation, and testing of GVs and SGVs have been emerging. Also, these measures have been finding novel applications in a variety of applied problems, ranging from such age‐old areas as agriculture and sample surveys to the modern developments in signal processing and Bioinformatics.
Generalized Canonical Variables
Authors: Sengupta Ashis 
Published: September 2014

AbstractOften the random vector variable, X, being encountered, for example, in atmospheric, biological, economic and environmental and other research, is of a large dimension but admits of meaningful grouping(s) into two or more mutually exclusive and exhaustive subvectors. Dimension reduction techniques are then sought to obtain “representative” new variables for each group to be formed by taking suitable compounds of the components within that group. This results in the substantially reduced dimensional vector variable Y to represent X. This greatly facilitates the associated computational and inferential statistical analyses. Hotelling's construction of canonical variables for the case of two groups of quantitative variables has been generalized in various directions to yield Generalized Canonical Variables that encompass more than two groups, both quantitative and qualitative components in X, order constraints imposed by the user on the compounding coefficients, and so on. The availability of packages makes the implementation of such techniques for real‐life problems quite feasible and attractive. On the inferential side, a variety of new types of hypotheses is posed: determination of optimal number of groups, best grouping for the same number of groups, and so on. While the corresponding distribution theory is quite involved, some interesting results are, nevertheless, emerging also. This topic is a fertile area and is in need of further theoretical and applied research, which should be very useful to a wide variety of practitioners.
Predicting Preclinical Disease by Using The Mixed‐Effects Regression Model
Authors: Sheng Shan L. , Brant Larry J. 
Published: September 2014

AbstractMost methods of disease prediction are generally restricted to logistic or Cox proportional hazards model regression using only baseline values of the risk factor, and where the individuals age and sex account for most of the predictive capacity of the method. This chapter presents a method of risk prediction appropriate for repeated measures or time‐dependent risk factors from longitudinal studies. The method is based on posterior probabilities calculated from mixed‐effects regression models to model intra‐ and inter‐individual differences in the serial risk factor measurements, to predict a disease‐related outcome or other future event according to age changes within gender groups. While aging is inevitable and the occurrence of disease increases with age, disease and aging are not synonymous. Disease is a selective event that occurs in only some members of the population; others maintain a greater degree of physiological function as they age and remain relatively disease free. Thus, longitudinal data that considers each individual as their own control and the use of an appropriate method of prediction can play an important role in promoting a healthy human life throughout the aging process.
Nonparametric (Distribution‐Free) Quality Control Charts
Authors: Chakraborti Subha 
Published: September 2014

AbstractControl charts that are typically based on the assumption of a specific form of a parametric distribution, such as the normal, are called parametric control charts. In many applications, however, there is not enough information to justify this assumption and control charts that do not depend on a particular distributional assumption are desirable. Nonparametric or distribution‐free control charts can serve this broader purpose. A key advantage of nonparametric charts is that its in‐control run length distribution is the same for all continuous process distributions. This means, for example, that the false alarm rate and the in‐control average run length of a nonparametric chart is the same for all continuous distributions. This is not true for parametric control charts in general and consequently their in‐control robustness can be a legitimate concern. Nonparametric charts are often more robust and efficient than their normal theory counterparts under heavy‐tailed and/or asymmetric distributions. In this paper we discuss developments, mostly in the area of univariate nonparametric control charts; this includes cases when the underlying parameters are specified as well as when they are unknown and are therefore estimated from the data. The majority of the charts are for monitoring the location; few charts are available for scale.
Chance—II
Authors: Mellor D. H. 
Published: September 2014

Abstract
This article is a critical survey of theories of chance, that is, of the objective empirical probabilities postulated by scientific theories of microphysics, evolution, genetics, and epidemiology. It distinguishes the chances from inductive and subjective probabilities; traces the development of theories of chance, from Laplace's eighteenth century classical theory to later frequency, limiting‐frequency and propensity theories; and sketches the epistemology of chance and explanations of why subjective probabilities should match known chances.
Continuum Regression
Authors: Sundberg Rolf 
Published: September 2014

AbstractWhen, in a multiple regression, regressors are near‐collinear, so‐called regularized or shrinkage regression methods can be highly preferable to ordinary least squares, by trading bias for variance. Continuum regression, introduced by Stone and Brooks in 1990, ties together several more classical regularized regression methods, such as principal components regression, partial least squares regression, and ridge regression.
Self‐Selected Samples
Authors: Rutherford Andrew 
Published: September 2014

AbstractThe term “Self‐Selected Samples” is explained. The nature of selection with respect to such samples is described, its relation to other sampling techniques is considered briefly and a distinction between strong and weak selection is made. Strong self‐selected samples are not representative of the population of interest and may very likely differ on pertinent factors. Hence, there are serious problems in generalizing inferences from self‐selected samples to their populations. A very brief outline is provided for three statistical approaches to problems with self‐selected samples: the use of covariates, mixture modeling, and selection modeling.
Human Capital
Authors: Dagum Camilo 
Published: September 2014

AbstractThis entry deals with the concept of human capital and its monetary value estimation. The basic assumptions and limitations of the traditional prospective (actuarial) and retrospective (cost of production) methods are discussed. A more efficient estimation procedure, the latent variable‐actuarial method, is presented and illustrated using information from the 1983 US Federal Reserve Board sample survey of income and wealth distribution. The main results obtained are the standardized value of each household HC latent variable, the average HC by age of the household heads, the average national HC, the monetary value of each household HC, and its size distribution.
Risk Measures and Economic Capital for (Re)Insurers
Authors: Tsanakas Andreas 
Published: September 2014

AbstractThis contribution relates to the use of risk measures for determining (re)insurers' economic capital requirements. Alternative sets of properties of risk measures are discussed. Furthermore, methods for constructing risk measuresviaindifference arguments, representation results, and reweighting of probability distributions are presented. How these different approaches relate to popular risk measures, such as value‐at‐risk, expected shortfall, distortion risk measures, and the exponential premium principle, is also dealt with. The problem of allocating aggregate economic capital to subportfolios (e.g., insurers' lines of business) is then considered, with particular emphasis on marginal‐cost‐type methods. The relationship between insurance pricing and capital allocation is briefly discussed, based on concepts such as the opunity and frictional costs of capital and the impact of the potential of default on insurance rates.
Decision Trees
Authors: Smith Jim Q. , Thwaites Peter 
Published: September 2014

AbstractA decision tree finds many uses in the representation and analysis of finite discrete models of risk. In this article, we review the different uses of this theory and compare it with some other frameworks of decision inference. We then illustrate how the decision tree can be used as a framework for an extensive form analysis, in the context of risks associated with decisions leading to the prosecution of a criminal court case.
Multiattribute Modeling
Authors: Stewart Theodor J. 
Published: September 2014

Abstract
We discuss the role of multiattribute modeling, or problem structuring, in the context of multicriteria decision making. Within this context, criteria for evaluating or comparing alternative decision options are represented in terms of operationally defined attributes describing the alternatives, on the basis of which specific objectives or goals may be defined. Attention is given to the process of eliciting the criteria relevant to a particular decision context by interaction with decision makers and/or other stakeholders, after which we turn to the practicalities of constructing an operationally meaningful set of attributes. A number of guidelines are presented for purposes of selecting a final set of attributes to be used in decision analysis, often structured into a hierarchical value tree. Finally, we discuss the manner in which conflicting views of different stakeholder groups need to be taken into consideration.
Societal Decision Making
Authors: French Simon 
Published: September 2014

Abstract
The context of decision making in the public sector is somewhat different from that in business and industry. Decision making may not be confined to a small management team or board of directors; but rather involves many politicians, officials, and different political institutions. There is a need to address many stakeholder concerns and be seen to do so, particularly when the decision involves major technological, health, environmental, or other risks. The issues addressed tend to be more complex and involve many factors and uncertainties. Here, we discuss how decision and risk analyses fit into such contexts and the differences between their approach and that of cost–benefit analysis.
History of Epidemiologic Studies
Authors: Samet Jonathan M. , Apelberg Benjamin J. 
Published: September 2014

AbstractEpidemiology can be defined as the scientific method used to assess health and its determinants in populations. Central to the method are measures of disease occurrence, and study designs for identifying causes of disease and quantifying their strength as causal agents. Because epidemiological studies directly address threats to human health, their findings, when available, have received emphasis in qualitative and quantitative risk assessments. Epidemiological data have the notable strength of being based on the “right” species, “real‐world” exposures, and often the full range of susceptibility. Observational data often have inherent limitations, but strategies are available to handle them. The development of radiation risk models from epidemiological studies and the use of the models in quantitative risk assessment have set a risk assessment paradigm grounded in epidemiological research.
Extreme Values in Reliability
Authors: Ferreira Ana 
Published: September 2014

Abstract
Extreme value (EV) theory provides probabilistic and statistical tools to deal with the largest or the smallest values of a sample, hence with tails of distribution functions. Applications of EV theory in reliability analysis can be found in many fields. For instance, in corrosion analysis, where pits of larger depth are of primary interest, as pitting corrosion can lead to the failure of metal structures, or, in strength of material problems where the principle of a chain being as strong as its weakest link dominates.
In the following text, main concepts of EV theory such as the maximum domain of attraction condition and EV distributions, peaks over threshold and the generalized Pareto distributions are discussed. Estimation within these frameworks, along with applications, are exemplified.
Hazard and Hazard Ratio
Authors: Ray Rose M. 
Published: September 2014

Abstract

The hazard function is a tool for describing the process of time to failure. Hazard functions arise in the context of survival analysis, also known as
failure analysis
, and provide a convenient way to visualize how the risk of failure changes with age or time. The exponential, Weibull, and lognormal distribution are three of the commonly used statistical models of time to failure. Hazard functions are used in epidemiology, medicine, ecology, engineering, and manufacturing settings, as well as in economics and finance. In this article, the mathematical concept of hazard and typical survival or failure time models are discussed.

The hazard ratio is a general way of examining relative risk. Cox proportional hazard models are examined, and nonparametric statistical methods for describing the relative risk of failure for two or more populations are discussed.
Fault Detection and Diagnosis
Authors: Gertler Janos J. 
Published: September 2014

Abstract
The article describes the detection and isolation (diagnosis) of faults (major equipment and sensor/actuator malfunctions) in engineering systems. The simpler, and less powerful methods do not rely on any mathematical model of the system; these include limit checking, special and multiple sensors, frequency analysis, and fault‐tree analysis. More advanced methods use mathematical models obtained from first principles or from experimental data. Such methods include on‐line parameter estimation, consistency checking, and principal component analysis. Two examples, one related to a simple electrical circuit and the other to a car‐engine subsystem, demonstrate the use of some of the methods.
Experience Feedback
Authors: Kjellén Urban 
Published: September 2014

Abstract
Experience feedback is the process by which information on the results of an activity is fed back to decision makers as new input to modify and improve subsequent activities. The main source of experience used in risk assessments is historic industrial systems. The experience emanates from incidents, failures, and normal operation, as well as from earlier risk assessments. Laboratory and field experiments provide complementary experience. Experience‐based knowledge from these activities is directed to the risk assessment team through different channels. The article distinguishes between channels for transfer of explicit and tacit knowledge. Experience databases and experience carriers play an important role in the first case, where experience carriers represent a higher degree of synthesis. If properly designed, the risk assessment team will constitute an arena for tacit knowledge exchange between risk analysts, personnel with operational experience, and systems designers. The article describes the processes for update of experience databases and experience carriers with new information. The need for documenting the experience basis in the risk assessment report is highlighted. An example from environmental risk assessments illustrates how experience feedback is achieved in practice.
Reliability Growth Testing
Authors: Pulcini Gianpaolo 
Published: September 2014

AbstractReliability growth is the process by which the reliability problems of a product are identified and corrected by introducing modifications in the product design and/or manufacturing process. Accordingly, the reliability of the product grows as the product goes through successive stages of the development program. Although reliability growth generally refers to improvements in a product's reliability, a very important field where reliability growth programs can find large application is the control of industrial accidents.We discuss a number of stochastic models that are able to describe the reliability growth, either when improvement actions are introduced at failure occurrence (test‐analyze‐and‐fix program), or when the corrective actions are delayed and implemented in block at the end of each stage. Such models are classified intocontinuousanddiscontinuousmodels according to the assumed behavior of the product reliability with test time. Special attention is devoted to the nonhomogeneous Poisson process with power‐law intensity function, namely, the power‐law process, which is the stochastic representation of the well known deterministic growth model of Duane.
Multistate Systems
Authors: Levitin Gregory 
Published: September 2014

Abstract
This article presents a general model of multistate system that can function at different levels of performances. The measures of risk associated with undesirable system behavior are defined on the basis of an acceptability function that represents the required relation between the system performance and a demand. A reliability block diagram method based on a universal generating function technique is presented as an efficient tool for evaluating the risk measures for complex multistate series–parallel systems. The risk associated with combination of internal failures and external attack is considered and a defense strategy methodology is discussed.
Structural Reliability
Authors: Stewart Mark G. 
Published: September 2014

Abstract
Structural systems such as buildings, bridges, water tanks, containment vessels, and communication towers are often represented as load‐resistance systems. In all cases, these structures represent substantial and expensive assets that are unique or “one‐off”. Even apparently, similar buildings have different occupancy loads, foundations, connection details, etc. Clearly, their reliability cannot be directly inferred from observation of failures or other experimental studies. In these circumstances, reliabilities need to be predicted from predictive models and probabilistic methods. As such, there is a clear recognition that uncertainty and variability are associated with many variables describing a structure's performance and that this can be accounted for explicitly by the use of probability distributions and structural reliability theory. Structural reliability theory allows the probability of failure to be predicted where failure is defined to occur when load exceeds a resistance. Not surprisingly, structural reliability theory underpins many recent advances in structural and safety engineering, including development of design and assessment codes, predicting service‐life performance, and assessment and maintenance of aging and deteriorating structures.
Failure Modes and Effects Analysis, applications
Authors: Price Chris 
Published: September 2014

Abstract
Failure mode and effects analysis (FMEA) is a safety analysis method that evaluates the effects of lower level failures on an overall system. The output of performing an FMEA is often ranked by significance, enabling engineers to focus on the failures with the most important effects. FMEA has its origins in the automotive and aerospace industries, but is now being applied in a much broader range of domains, from medical systems to semiconductor manufacturing.
Data Fusion
Authors: Braverman Amy J. 
Published: September 2014

AbstractWe define data fusion as the process of combining statistically heterogeneous samples from marginal distributions to construct a new sample that can be regarded as having come from the unobserved joint distribution of interest. This problem has been addressed in the literature on statistical matching, which also appeals to the missing data paradigm of Little and Rubin. Though less obvious, spatial statistics also offers techniques that facilitate rigorous data fusion by inferring unobserved values in multiple data sets at specific locations. We touch just briefly on the topic of copulas. Copulas have yet to be exploited in this context, but the link is clear given our definition of data fusion. Finally, we consider some challenges posed by data fusion, and maybe more importantly by the absence of data fusion.
Randomized Controlled Trials
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana 
Published: September 2014

AbstractIn the medical sciences, the ideal experiment is the randomized controlled clinical trial (RCT) in which the subject or patient is randomized to treatment with an experimental agent or control (either placebo or other active treatment), in a double‐blind manner. The RCT is the “gold standard” in medical research to establish the efficacy of a new treatment or intervention. The benefits of such an experiment are that any observed differences between the two groups (or more groups) with respect to the outcome can be attributed to the treatment and are without bias. That is, confounding is controlled and selection bias is removed since neither the subject nor the investigator chooses the intervention.In this entry, we review the issues in the design, conduct, analysis, and interpretation of a randomized clinical trial.
Clinical Dose‐Response Assessment
Authors: Burman Carl‐Fredrik 
Published: September 2014

AbstractEssentially all pharmacologically active drugs give adverse effects at high enough doses. On the other hand, the intended effect is generally increasing with dose. A critical question is, therefore, which dose should be chosen, that is, which dose minimizes the total risk associated with both insufficient therapeutic effect and side effects. This article briefly describes the estimation and weighting of different risk components, as well as the design of dose‐finding trials.