bluuebunny/crossref_metadata_2025_split huggingface dataset:
Kriging
Authors: Switzer Paul 
Published: September 2014

Abstract
Kriging is a method for spatial interpolation based on correlation modeling between observations. This article describes how correlation is used to assign weights to control data for interpolations, and how correlation is used to assess the uncertainty of interpolation. Additionally, the article describes how auxiliary and prior information are used in kriging interpolation and how such information constrains the weights assigned to the control data. Finally, the article describes the impact of measurement error and short‐scale variability on interpolated values and on the interpolation uncertainty. An illustration indicates how control data are weighted differently according to their relation to the interpolation location and how interpolation uncertainty would change with increasing density of the control data.
Public Health Surveillance
Authors: Stoto Michael A. 
Published: September 2014

AbstractPublic health surveillance seeks to assess and track the health of the public, define public health priorities, evaluate programs, and stimulate research. It includes case surveillance focuses on individuals, or sometimes groups of individuals, to identify individuals with certain diseases and take action to stop spread of disease. Statistical surveillance, on the other hand, focuses on populations to identify differentials and trends that can inform public health policymaking, including the allocation of resources. Surveillance systems rely on a diversity of data sources. Surveillance of infectious disease relies primarily on the system of notifiable diseases put in place in the nineteenth century. Statistical surveillance systems use data from vital statistics, population‐based sample surveys, registries, and health care records.The introduction and growth of syndromic surveillance following 9/11 has focused on a statistical approach to detecting bioterrorist attacks. Despite many accomplishments, the value of syndromic surveillance for detecting bioterrorist attacks has not yet been demonstrated. Ultimately, the most important contribution of syndromic surveillance systems to public health may be for natural disease outbreaks, such as seasonal and pandemic influenza, and for other public health purposes beyond simple detection.
Case‐Control Studies: Overview
Authors: Rothman Kenneth J. , Greenland Sander , Lash Timothy L. 
Published: September 2014

Abstract
In a cohort study, the numerator and denominator of each disease frequency (incidence proportion, incidence rate, or incidence odds) are measured, which requires enumerating the entire population and keeping it under surveillance. A case‐control study observes the population more efficiently by using a sample of the population, which becomes the control series, in place of complete assessment of the denominators of the disease frequencies. This extra sampling step can make a case‐control study much more efficient than a cohort study of the same population, but it introduces a number of subtleties and avenues for bias that are absent in typical cohort studies. For diseases that are sufficiently rare, cohort studies become impractical, and case‐control studies become the only useful alternative.
The cases in a case‐control study should be the same people who would be considered cases in a cohort study of the same population. The definition of the source population for these cases determines the population from which controls should be sampled. Ideally, control selection would involve direct sampling from the source population, with equal probability of selection, although in many circumstances, variations in and substitutes for this ideal sampling design may be considered.
Model Risk
Authors: Crouhy Michel , Galai Dan , Mark Robert 
Published: September 2014

Abstract
Models are an inevitable feature of modern finance, and model risk is inherent in the use of models. In this article we stress the technical elements of model risk. Models are susceptible to errors: from incorrect assumptions about price dynamics and market interactions, from implementing a model wrongly, from inaccurate estimation of volatilities and correlations and other inputs that are not directly observable and must be forecasted. We also discuss how to mitigate model risk.
Volatility Modeling
Authors: Andersen Torben G. 
Published: September 2014

AbstractThis chapter discusses the main approaches to return volatility modeling based on historical time series observations on the underlying price. It starts from simple filtering estimates including historical volatility measures and exponential smoothers used by Risk Metrics, then progresses through ARCH and more general stochastic volatility modeling procedures and ends with the recent development of realized volatility measurement, modeling and forecasting based on the increasing availability of high‐frequency intraday transaction and quote data from financial markets.
Volatility Smile
Authors: Duan Jin‐Chuan , Li Yun 
Published: September 2014

Abstract

Although the Black–Scholes model lays the foundation to the modern option pricing theory, its empirical performance is rather unsatisfactory as manifested in the phenomenon known as
volatility smile
. This article explains what volatility smile is, and describes some nonparametric techniques and parametric models that may be used to deal with volatility smile for pricing and/or hedging purposes. Kernel regression and the GARCH option pricing model are chosen to be the nonparametric technique and parametric model, respectively, in our demonstration involving a sample of S&P 500 index options. Kernel regression is found to perform better than the GARCH model, but its applicability is limited to pricing the same type of options. Without providing a dynamic for the underlying asset price, kernel regression is also ill‐suited for hedging purposes. In contrast, the GARCH model is not subject to the same application limitations even though its performance on the same type of options is poorer. Thus, both nonparametric techniques and parametric models serve useful purposes in dealing with derivative contracts.
Structural Models of Corporate Credit Risk
Authors: Wong Hoi Y. 
Published: September 2014

Abstract
Credit risk is related to the ability of a corporation or an individual to honor a financial obligation. A typical example is the default risk embedded in corporate bond markets. Structural models of credit risk are specifically designed to describe corporate credit risk using the capital structure of a firm. The capital structure comprises the firm's underlying assets, equities, and debts. A comprehensive approach even takes into account the optimal allocation of the firm's assets, the dividend policy of the management, and protective covenants of bond holders. The Moody's KMV, a corporation owned by the Moody's Investor Services, is the first rating agency to put a structural model into commercial use since the early 1990s. Popular structural models include, but are not limited to, the Merton model, the Black–Cox model, the Leland–Toft model, and the KMV approach. In this article, a brief overview of the formulation of a structural approach, specifications within the aforementioned models, and the implementation issues is given.
Dynamic Financial Analysis
Authors: D'Arcy Stephen P. 
Published: September 2014

Abstract
Dynamic financial analysis (DFA) is an approach used by organizations to model financial results of the entity under a variety of conditions. The modeling techniques embedded in DFA developed as advances in the computational power of business computers made running large numbers of complex simulations feasible. The models have gradually become more complex, more realistic, and more widely applicable. Companies, rating agencies, and regulators now routinely use DFA type models to evaluate the financial condition of an organization and assess operational changes. This article describes this approach, compares simulation with other financial planning methods, and provides the details of a publicly accessible DFA model that has been extensively used in research and education. DFA is most widely used by insurance companies and other financial institutions, but many nonfinancial firms utilize similar techniques.
Environmental Health Risk Assessment, Statistics in
Authors: Stern Alan H. 
Published: September 2014

AbstractQuantitative health risk assessment is a tool for making predictions about the impact of substances in the environment on health. Despite the use of scientific principles and a reliance on the most appropriate data from the scientific literature, risk assessment, in the strict sense of the word, is not, by itself, a science. Two different forms of quantitative risk assessment are practiced. One form can be referred to assafety assessment, which seeks to identify the largest level of exposure or dose that can be reasonably expected to be without adverse effect. The other form seeks to quantify the risk associated with any exposure or dose of an environmental agent. In the United States, this form of risk assessment is generally applied to carcinogens. The classical structure of formal environmental health risk assessments comprises of four interdependent steps: hazard identification; dose–response assessment; exposure assessment; and risk characterization. Recent advances in conducting environmental health risk assessments include the use of the benchmark dose approach in lieu of the no observed adverse effect level (NOAEL)/lowest observed adverse effect level (LOAEL) determination, the use of probabilistic analysis in place of single value estimates for risk assessment variables, and progress toward harmonization of cancer and noncancer endpoints.
Role of Risk Communication in a Comprehensive Risk Management Approach
Authors: Jardine Cynthia G. 
Published: September 2014

Abstract
Risk communication is now seen as a reciprocal exchange of information between all interested and affected parties that serves to inform them about the risk issue. It is also generally acknowledged that successful risk management requires the incorporation of risk communication as an integral component of the risk process, from the critical stage of problem formulation through to the final selection of risk‐management options. However, while this knowledge is becoming more generally accepted, it is not being consistently employed in practice.
Evaluation of Risk Communication Efforts
Authors: Jardine Cynthia G. 
Published: September 2014

Abstract
Evaluation is a critical component of risk communication. However, it is frequently overlooked when planning and implementing risk communication efforts. The many compelling reasons for conducting evaluation and the various types of evaluation are explored. Some key factors required for successful evaluation are also discussed. Finally, some recognized barriers to conducting evaluation are presented. Evaluation needs to be considered as a required component of any risk communication program, or we doom ourselves to forever repeating the same mistakes instead of building on our successes.
Laplace Approximations inBayesian Lifetime Analysis
Authors: Onar Arzu 
Published: September 2014

AbstractWe provide some background on Laplace approximations based on the work by Tierney and Kadane and illustrate the method's use in obtaining posterior distributions as well as the associated parameter estimates in Bayesian lifetime analysis. The advantages and the disadvantages of using this approach compared to other numerical alternatives are also discussed. Finally, a few examples from the relevant literature where the approximation has been successfully employed are provided.
M
onte
C
arlo Methods, Univariate and Multivariate
Authors: Walsh Cathal D. 
Published: September 2014

Abstract
With the prevalence of high‐powered computing, Monte Carlo methods are increasingly used in a wide range of disciplines. In this article we review some of the principles behind these methods, note the relationship with other techniques used in systems reliability, and demonstrate some methods in practice. In particular, the accept–reject algorithm, the importance sampler, the method of compositions and the specific case of Box–Muller transformation are highlighted here.
Life Distributions, Simulation of
Authors: Marseguerra Marzio , Zio Enrico 
Published: September 2014

AbstractThis chapter introduces the principles underlying the simulation of the probability distributions that typically characterize the life or failure processes of engineered components and systems. The basic procedures for sampling random numbers are reviewed and examples are provided with reference to the uniform, exponential, and Weibull distributions, which are among the most commonly used probability distributions in reliability, availability, and safety analysis.
Normality Tests: Comparisons
Authors: Koning Alex J. 
Published: September 2014

Abstract

For testing normality with unknown parameters μ and σ
2
, extensive simulation studies have shown the superiority of the Shapiro–Wilk test and variants. The Shapiro–Francia test is a simplified version of the Shapiro–Wilk test, which uses the squared normal probability plot correlation as a test statistic. The Cramér–von Mises and Anderson–Darling tests with plug‐in estimators for μ and σ
2
perform slightly worse than the Shapiro–Wilk test.
Failure Modes and Effects Analysis, Implementation of
Authors: Bowles John B. 
Published: September 2014

Abstract
Failure modes and effects analysis (FMEA) is potentially one of the most beneficial and productive tasks in a well structured reliability program. FMEA consists of examining the modes and causes of potential item failures and determining the product response to the failure. Steps can then be taken to change the design to eliminate the failure, mitigate its effects, or develop compensating provisions if the failure should occur. FMEA can be applied to hardware, software, material, and process related causes of failure. Functional, interface, and detailed FMEAs can be usefully employed throughout the design process from the concept stage to production and deployment.
Lifetime Distributions, Optimization Methods for
Authors: Hayes Kevin , Barry Don 
Published: September 2014

Abstract
This article introduces the likelihood function as the main tool for parameter estimation and statistical inference with lifetime distributions. When the likelihood function cannot be maximized directly, numerical optimization is required. Approaches based on the Newton–Raphson algorithm are discussed and modifications used to ensure properties such as convergence to maxima rather than minima or saddle points, calculation of nonnegative parameter estimates, and invertibility of the Fisher information matrix are considered. The variability of parameter estimates, generalized linear models, and stochastic search routines are also briefly mentioned.
Availability Models
Authors: Aven Terje 
Published: September 2014

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.
Subjective Life Models
Authors: Mendel Max 
Published: September 2014

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.
Characterization in Reliability
Authors: Klebanov Lev B. 
Published: September 2014

Abstract
We describe the problems of characterization of probability distributions arising in reliability theory. These characterizations are mostly based on the property of aging of technical devices or systems. Among them are the classical lack of memory property and its generalizations, functioning properties of redundant systems, relations between the reliabilities of two systems, and reliability of hierarchical structures. Typical results on the characterizations and their stability inside some popular classes of distributions with the special character of the hazard function are also given.
Precedence Tests
Authors: Balakrishnan Narayanaswamy , Tony Ng Hon Keung 
Published: September 2014

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.
Design and Testing of Questionnaires
Authors: Ograjenšek Irena 
Published: September 2014

Abstract

The questionnaire is the most frequently used data collection tool of survey research. It can be applied in personal, telephone, mail, and on‐line surveys. Two factors which determine the questionnaire design requirements to the largest extent are: the form of the questionnaire (paper
versus
electronic), and the mode of questionnaire administration (interviewer‐
versus
self‐administered questionnaire).

In order to test and evaluate a questionnaire, a wide range of methods can be used. Some (e.g., an expert panel or peer evaluation) are more applicable in the early stages of questionnaire design, other (e.g., a pilot study) should be used prior to the broad questionnaire distribution.
SERVQUAL
Surveys
Authors: Ograjenšek Irena 
Published: September 2014

Abstract
Service quality is often analyzed using rating scales to assess individual service attributes. The most widely used and most carefully scrutinized rating scale is SERVQUAL. The scale is a result of a systematic ongoing study of service quality that begun in 1983. The model defines quality as the difference between customer perceptions and expectations with regard to quality of delivered service. The respondents are asked to answer two sets of questions dealing with the same subject, one set at a general level, and one for a company in question. Respondents choose from a modified Likert scale which measures intensity of agreement or disagreement with any given statement. Response options range from “strongly agree” and lesser levels of agreement to “neutral” (neither agree nor disagree) and lesser levels of disagreement to “strongly disagree”. Although SERVQUAL has been widely used in business‐to‐business and business‐to‐customer settings, this does not mean the scale has not been subjected to reexamination and criticism. There are several well researched and documented objections to SERVQUAL. As a consequence of these critique, various alternative formulations of the service quality model have been suggested. The only one that, to date, seems to outperfom SERVQUAL is SERVPERF, the performance‐only measure of service quality.
Clinical Behavior Change
Authors: der Weijden Trudy van , Wensing Michel 
Published: September 2014

Abstract
Many patients do not receive optimal care. Clinical behavior change is directed at improvements in patient care. A systematic approach to clinical behavior change is promoted. Research on clinical behavior change is the scientific study of methods to promote the systematic uptake of clinical research findings into routine practice. Both quantitative and qualitative approaches in data collection can contribute to the development and evaluation of interventions targeted at clinical behavior change. This chapter focuses on the collection and analysis of quantitative data to answer questions such as “What is current practice?” and “What is the effect of an implementation strategy?” There are several specific statistical issues in this research area, such as the validity of measurements of clinical behavior, interdoctor variation, and clustering effects affecting power calculation and level of analysis. These issues are discussed in this chapter, and illustrated with examples from a study describing the changing test‐ordering behavior of general practitioners.
Quality Management, Overview
Authors: Watson Gregory H. 
Published: September 2014

Abstract
Quality management involves assessing, planning, improving, and controlling work that people accomplish to achieve their shared objective of service to their customers. Quality management requires attention to detail at each level of the organization: leaders must focus on concerns of shareholders and the community; managers must focus on commercial objectives for their targeted markets; and operational teams must focus on the daily work routine of the organization. Each level is responsible for contributing a unique element of the overall quality program. Quality management delivers a consistent system of working that establishes criteria for challenging aspirations for the desirable behavior of the organization, plans improvement objectives based on gaps in performance between the desired state of performance and the current observations of performance, improves work by executing projects that change the way that the routine work processes perform, and defines standards of performance for continuing control of these routine work processes. The components of a quality management system are usually derived from the Malcolm Baldrige National Quality Award criteria for business excellence, external benchmarking studies, total quality management, Six Sigma, and lean production methods, and then these components are packaged into a structure that adheres to the ISO 9000 standard for a quality management system (or other appropriate quality system structure).
Benchmarking in Project Definition
Authors: Watson Gregory H. 
Published: September 2014

Abstract
Benchmarking is a process of comparing performance between two or more organizations about a product, process or service that they have in common. The benchmarking objective is to identify a process that has superior performance and to determine the reasons behind that exceptional performance. Benchmarking studies may be conducted at either the strategic, or operational organizational level and they may compare competitive organizations, internal divisions or processes, industry‐wide practices, or generic business practices in cross‐industry studies. Benchmarking follows a four‐step process: plan‐collect‐analyze‐improve. In the planning step the objective of the study is defined and a literature search is made to determine what information is available from the public domain. Companies are targeted as partners in the study and a preliminary questionnaire is developed in order to solicit their participation. In the second step of the study surveys are conducted, site visits are accomplished, and interviews are used as part of the data collection process. In the analyze step, statistical and practical comparisons are made and best practices are identified among the participating organizations. In the final step, potential enablers of improvement are culturally adapted to each organization and they are implemented into the daily management system.