bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data Mining, Evaluation Techniques in
Authors: Gür Ali Özden F. 
Published: September 2014

Abstract

In this article, we focus on evaluation techniques for data mining models after feature selection and parameter estimation have already been performed. Though performance evaluation techniques for data mining models are typically independent of the data mining task, goodness measures
are
task specific. We discuss the rationale, and the pros and cons of different goodness measures by data mining task, using illustrative examples to explain the concepts. We cover the following data mining tasks: classification, regression, rank prediction, clustering, and market basket analysis tasks. We provide detailed discussions for accuracy measures such as misclassification rate, confusion matrix, misclassification cost, quadratic and informational loss, ROC curve and lift chart, RMSE, MAD, AIC, BIC, Spearman's ρ, and Kendall's τ, within and between cluster distance. The following performance estimation techniques are discussed: resubstitution, holdout,
v
‐fold cross‐validation, leave‐one‐out/jackknife, and bootstrap. Further, we cover statistical hypothesis testing for comparing performance of two algorithms for the same data mining task. Also, references for further reading on the topics covered in this article are provided.
Homogeneity of Variances
Authors: Koning Alex J. 
Published: September 2014

Abstract
The lack of homoscedasticity affects the validity of statistical tests, and has a negative effect on the coverage of confidence intervals and on the accuracy of estimators. Formal diagnostic tests and diagnostic plots are available for analysis of variance, the linear regression model and the nonparametric regression model. Box–Cox transformations are used to reduce the lack of homoscedasticity.
Heteroscedasticity
Authors: Morrison Donald F. 
Published: September 2014

Abstract

Homoscedasticity is the condition under which a series of observations have the same degree of variation. Heteroscedasticity is its negation. Homoscedasticity is assumed in the standard models for least‐squares linear regression, the two‐sample
t
test and the analysis of variance, and the effects of heteroscedasticity in these situations are discussed here.
Fault Tree Analysis for Large Systems
Authors: Andrews John D. 
Published: September 2014

Abstract
This article describes the binary decision diagram (BDD) technique of fault tree analysis. The BDD provides an alternative to the traditional fault tree analysis method of kinetic tree theory which is progressed in two phases. The first phase is a qualitative stage to determine the minimal cut sets. The second stage then uses the minimal cut sets together with the component failure probabilities to conduct the system quantification. For large fault trees, this process is inefficient.
While fault trees express the system failure logic in a convenient way from the engineering viewpoint, they do not lend themselves to the mathematical manipulation. BDDs express the failure logic in a disjoint form, which makes quantification very fast. The calculations require just one pass through the BDD structure. Minimal cut sets are not required as an intermediate step, although there are methods by which they can be obtained if they are of interest. Calculations are exact and do not need any form of approximation. The trade‐off for the gains in speed and accuracy is that the fault tree has to be converted to a BDD. The advantages of this are more evident on large, complex fault tree structures.
Survival Analysis, Nonparametric
Authors: Kottas Athanasios 
Published: September 2014

Abstract
Survival analysis was one of the first areas where Bayesian nonparametric priors were utilized to enhance the flexibility of standard parametric models. Bayesian nonparametric methods are very well suited for survival data analysis, enabling flexible modeling and rich inference for the survival function or hazard function, providing techniques to handle censoring, and allowing incorporation of prior information. In this article, we provide a review of Bayesian nonparametric and semiparametric approaches, based on mixture models, to the analysis of univariate survival data. We discuss mixture models for survival distributions in a fully nonparametric setting as well as for error distributions in semiparametric accelerated failure time regression settings.
Degradation and Failure
Authors: Nikulin Mikhail 
Published: September 2014

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: Overview
Authors: Lindqvist Bo Henry 
Published: September 2014

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.
Damage Processes
Authors: Singpurwalla Nozer D. 
Published: September 2014

AbstractThe 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 asmarkers. 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 evolutionviaa 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 thehazard potentialof 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.
Software Reliability:Bayesian Analysis
Authors: Wiper Michael P. 
Published: September 2014

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: September 2014

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.
Bayesian Design of Reliability Experiments
Authors: Meeker William Q. , Zhang Yao 
Published: September 2014

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.
Maintenance andMarkov Decision Models
Authors: Dekker Rommert , Nicolai Robin P. , Kallenberg Lodewijk C.M. 
Published: September 2014

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.
Masked Failure Data:Bayesian Modeling
Authors: Kuo Lynn 
Published: September 2014

AbstractIn the analysis for masked system lifetime data, the exact component that causes the system's failure is often unknown. For each series system at test, we observe its system's failure time and a set of components that includes the component actually causing the system to fail. The objective is to make inferences for the reliability of the components and their diagnostic probabilities. Basic Bayesian models are reviewed. Typically, both Expectation and Maximization (EM) and Markov Chain Monte Carlo (MCMC) algorithms are employed for inferences.Both exponential distributions and Weibull distributions for the component lifetimes and some masking probability models are used here to illustrate these techniques.
Multivariate Stochastic Orders and Aging
Authors: Belzunce Félix , Shaked Moshe 
Published: September 2014

Abstract
Many aging properties of univariate random lifetimes can be characterized by various stochastic orders. As a result, several efforts have been made in the literature to define or characterize aging notions of random vectors of lifetimes by means of multivariate stochastic orders. Such definitions enable researchers to apply the intuition that comes with the univariate aging notions to complex systems. The 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 definitions and characterizations.
Scan Statistics in Quality and Reliability
Authors: Glaz Joseph 
Published: September 2014

Abstract

Scan statistics have been used extensively in several areas of science and technology to test for the occurrence of clusters of rare events. In this article we present a survey of results in the area of scan statistics that are applicable to quality control and reliability theory. In quality control, scan statistics have been used in the design and analysis of acceptance sampling schemes. We survey results that are useful in evaluating the operating characteristics of such sampling schemes. In reliability theory, scan statistics have been used in the analysis and design of a
k
‐within‐consecutive‐
m
‐out‐of‐
n
:
F
system of
n
components arranged in a linear or circular fashion. This system fails if
k
components fail within any segment of
m
consecutive components. In this article, we survey results for approximations and bounds for the system reliability of such systems. Two‐dimensional scan statistics have been used in the analysis and design of a
k
‐within‐consecutive‐(
m
1
,
m
2
)‐out‐of‐(
n
1
,
n
2
):
F
system of
n
1
n
2
components arranged in an
n
1
by
n
2
rectangular or cylindrical lattice. This system fails if
k
components fail within an
m
1
by
m
2
rectangular subregion in the
n
1
by
n
2
lattice. We survey results for the approximations and bounds for system reliability of these systems. In material science, reliability of certain materials can be compromised by the appearance of a large number of microcracks in a small area or volume. We present a survey of two‐ and three‐dimensional scan statistics that have been used in analyzing the occurrences of such microcracks.
Intercalibration Studies
Authors: Barry Jon , Allen Yvonne 
Published: September 2014

Abstract

The Biological Effects Quality Assurance in Monitoring Programmes (BEQUALM) scheme was initiated to develop quality standards for a range of biological effects techniques and to monitor compliance of laboratories generating data from these techniques for national and international monitoring programs (e.g. Oslo and Paris Commissions) and also for regulatory purposes. Each year, the lead laboratory (Cefas) organises a program of intercalibration exercises for each of the methods included in the scheme. In this paper, the results from a 2005/2006 intercalibration using a 48‐h acute test with
Daphnia magna
(water flea) are described.

Each intercalibration exercise is divided into two parts. In the first, laboratories were given an unspecified concentration of a reference chemical (zinc sulphate) which they were asked to dilute by a given factor and estimate the toxicity of the substance in terms of the percentage mortality. In the second part, a predetermined dilution series of the reference chemical was prepared by each laboratory from a provided stock and mortality at each concentration measured. From these mortalities, the medial lethal concentration (EC
50
) was estimated for each laboratory. We describe how these results are analyzed and, in particular, how compliance of laboratories is evaluated. The aim is to bring about continuous improvements in the methods adopted by the laboratories taking part.
Disease Surveillance
Authors: Lawson Andrew B. 
Published: September 2014

AbstractThis work focuses on disease surveillance and issues related to prospective detection of changes or aberrations in health data. One feature of recent interest in this area is the use of ancillary variables to allow for early detection of outbreaks. For example, gastro‐intestinal disease outbreaks can be detected by examining over‐the‐counter sales of pharmaceutical products. This work is sometimes known as syndromic surveillance. The focus of the detection will define the correct methods used in detection. For example, if a cluster of risk is to be detected then different methods would be used that if a jump in risk over time were to be detected.
Healthcare Policy and Management of Stroke, Breast Cancer, and Mental Health
Authors: H. Lakhani Azim D. 
Published: September 2014

Abstract
This brief article shows the kinds of health‐outcomes related questions that policy makers and managers ought to be asking about health care, such as levels of risk to health, levels of disease and ill health, potentially avoidable adverse health events, health‐related quality of life, and premature death. The focus is on what is achieved through resource use. The role of statistics in monitoring this, and the constraints in the availability and use of statistics have been illustrated using data on stroke, breast cancer, and mental health from the National Health Service in England. Trends in a variety of indicators such as obesity, treated and controlled high blood pressure, cancer incidence, cancer screening, hospital admissions, timely discharge, emergency readmission after discharge, and premature deaths show improvement over years but there is still room for further improvement. The data also show variation by geography, gender, and social deprivation. Aspects of monitoring not covered by routine data and ways of filling in gaps are described. Information on outcomes is one part of wider monitoring, covering the organization of services, resources available, access to and quality of service delivery, and patient perspectives, including their satisfaction with the services provided. This article is of necessity, an oversimplification of what are in reality extremely complex systems and issues.
Self‐Controlled Case Series Analysis
Authors: Hocine Mounia N. , Whitaker Heather J. , Farrington C. Paddy 
Published: September 2014

Abstract
The case series method was developed by Farrington 1 to investigate the strength of association between a time‐varying exposure and a potentially recurrent adverse event, using cases only. The method has been widely used in pharmacoepidemiology, particularly in the study of vaccine safety. The method is derived from an age‐dependent Poisson model by conditioning on the number of events and on exposure histories. The conditioning leads to estimation within individuals, and thus the effects of fixed covariates cancel out. In some circumstances, the case series method has high efficiency, relative to the cohort method. A semiparametric version of the case series method, in which the underlying age effect is left unspecified, is also available.
Patient Opinion Measures
Authors: Wensing Michel 
Published: September 2014

Abstract
This contribution aims to describe some statistical aspects of the application of patient opinion measures in healthcare. Examples of these applications are educational feedback to healthcare providers, use in formal accreditation procedures in care providers, and public reporting on patient opinions. While some patient opinion measures have been validated to some extent, statistical issues related to their application tend to be ignored. Three domains are discussed in this contribution: the sampling of study participants (procedures, response rates, statistical power), the handling of items (scale construction, missing values), and the presentation of findings (confounders, uncertainty). The Europep instrument is used throughout the paper for illustration of the points. This is a 23‐item questionnaire of patient evaluations of general practice, which has been widely used across Europe. Statistical aspects of applying patient opinion measures should be addressed to avoid bias, also if the measure itself was carefully developed and validated.
Bias in Modeling and Monitoring Health Outcomes
Authors: J. Grigg Olivia A. 
Published: September 2014

Abstract
The issue of bias is discussed in relation to the combined activities of both modelling health outcomes and monitoring them. As a motivating example, six‐monthly infection rates over the period April 2001 to September 2005 of MRSA in a UK NHS Trust are considered. Given a signal in the interval October 2003 to March 2004 of a potential increase in the underlying infection rate, it is described and discussed in detail how one might go about re‐estimating the rate of infection and perhaps adjusting it for bias.
Error Proofing, Healthcare
Authors: Nakajo Takeshi , Clapp Timothy G. , Godfrey A. Blanton 
Published: September 2014

Abstract
Many error proofing solutions are implemented successfully in healthcare. Most of them, however, are generated individually without the benefit of the knowledge of the wider body of error‐proofing solutions in related healthcare areas. Almost no healthcare organization has the benefit of the extensive experience in error proofing in manufacturing industries or other service industries. This article provides a simple methodology for applying error proofing principles and proven healthcare solutions for systematically generating workable solutions to reduce human error.
Cost of Quality
Authors: Godfrey A. Blanton , Moore Joseph D. 
Published: September 2014

Abstract
The understanding of costs of quality has evolved considerably over the past few decades. Traditionally, quality costs were thought of as prevention, appraisal, internal failures, and external failures. We also thought there was an optimal level of quality where costs were minimized. Newer thinking of quality costs has questioned the concept of optimum level, added the concept of quadratic loss, and explored many other costs associated with the costs of poor quality. Often, these new costs are far, far greater than the traditional costs.
Target Costing
Authors: Faltin Donna M. 
Published: September 2014

Abstract
Target costing is a proven, market‐driven strategy for product and service design. First conceived in the 1960s in Japan, target costing expanded the concept of value engineering to a customer‐focused, profit‐driven cost system that focuses on price.
Traditional “cost plus” reasoning sets the price of a product or service by starting with anticipated or realized costs, and adding the desired profit margin. This approach risks setting prices at a level the market will not support, with consequent risk of losing part or all of the capital invested in the introduction of a new product or service. Target costing, on the other hand, starts with the price that market conditions warrant, and factors in desired profitability to yield a “target cost” that must be met in the design and delivery of a product or service. By starting from market realities and explicitly accounting for business needs, target costing makes business success a design requirement, and helps avoid costly financial disasters in new product or service introduction.