bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modifiable Areal Unit Problem (
MAUP
)
Authors: Sexton Ken 
Published: September 2014

Abstract
Deciding on an appropriate research design, interpreting study results, or choosing a suitable public policy to mitigate environmental risk implies a decision, either implicit or explicit, about spatial scale and resolution. The modifiable areal unit problem (MAUP) is a formal statement of the dilemma facing researchers, risk assessors, and policy makers when they use geographical data—results of statistical analyses usually depend on subjective decisions about geographical units of analysis and the level of data aggregation within those units. Consideration of the MAUP is essential for the development of effective and efficient strategies for managing environmental health risks.
K
ano Analysis
Authors: Shenk Debra 
Published: September 2014

Abstract
In the early 1980s, Dr. Noriaki Kano of Tokyo Rika University introduced several concepts aimed at a better understanding of quality as the customer defines it. He described a continuum over which a customer assigns a valuation to a particular feature or attribute of a product or service. These concepts include the “attractive quality” and “must‐be quality”. When properly implemented, an understanding of these methods can lead companies to define sets of features that will fulfill customer needs and grow market share.
Response Surface Methodology
Authors: Steinberg David M. , Kenett Ron S. 
Published: September 2014

AbstractResponse surface methodology is a general strategy for combining designed experiments and regression analysis to explore the relationship between one or more response variables and a set of factors that are thought to affect the responses. Some of the key elements in this strategy include sequential progress, a matching of experimental designs to the complexity of needed regression models and rapid progress to the most interesting parts of the factor space. These methods are often used to seek optimal settings of the factors for reaching specific values of the response, such as a maximum or minimum value. Response surface methods are widely used in research and development and industrial applications.
Sequential Experimentation
Authors: Gunter Bert 
Published: September 2014

Abstract
Sequential experimentation is the application of statistical experimental design methods to improving processes when many experimental factors must be studied. It emphasizes the sequential use of small two‐level designs and steepest ascent to identify critical factors and improved settings, and the sequential assembly of second‐order designs to elucidate the nature of the response surface in the improved operational region when necessary.
Statistical Process Control, Multivariate
Authors: Fuchs Camil , Kenett Ron S. 
Published: September 2014

AbstractOwing to pervasive capabilities of modern data collection, analysis, and graphical displays, the multivariate statistical process control is playing an increasingly central role in industry and services. Its application requires careful consideration of the data context and the statistical tools involved. In this section we present various situations in which such techniques are used and provide details on their practical implementation.
Sampling in Semiconductor Manufacturing
Authors: Barlović Robert , Holfeld Andre , Räbiger Jan 
Published: September 2014

Abstract
The task of sampling in state‐of‐the‐art semiconductor manufacturing is to provide relevant metrology data for a representative part of the population in order to monitor and maintain the process, thereby minimizing the risk of processing errors/faults. The decision process itself is restricted by certain expense criteria. Thus, sampling has to select the appropriate material for metrology based on manufacturing needs while fulfilling manufacturing constraints.
Random Walk
Authors: Doney R.A. 
Published: September 2014

AbstractRandom walks are the simplest of all stochastic processes; they include amenable examples of both Markov processes and martingales, and indeed the genesis of these two important classes of processes can be found in their study. Their origins lie in the first attempts to model gambling situations, and they still have many useful modeling applications. In actuarial mathematics, random walks occur widely in ruin theory. In discrete time, the surplus process (claims minus premiums) may be modeled as a random walk. In continuous time, the surplus observed at claim times is a random walk, for example, for the Sparre Andersen model. In both cases, the ruin probability isP(M > u), whereuis the initial reserve andMdenotes the all‐time maximum of the random walk. The study ofMalso occurs in queueing theory, since it has the same distribution as the waiting time in aGI/G/1queue.
Diffusion Approximations
Authors: Schmidli Hanspeter 
Published: September 2014

AbstractCalculations of characteristics of stochastic processes, as for example, ruin probabilities, is often difficult. One therefore has to resort to approximations. Approximation of the process at a fixed point often suggests by the central limit theorem to use a normal approximation. Approximation of the whole process should then result in a Gaussian process. In many situations, the Brownian motion is the natural choice.
Insurance Regulation and Supervision
Authors: Tess Daniel 
Published: September 2014

Abstract
Aside from normal regulatory activities, companies and other entities that sell or provide insurance products are regulated in two special ways. Solvency regulation concerns insurers' financial ability to pay future claims against policies sold, and price regulation concerns the level of premiums that are charged to policyholders. Actuaries generally play a key active role in both of these regulatory systems.
Affine Models of the Term Structure of Interest Rates
Authors: Webber Nick 
Published: September 2014

Abstract
Several families of interest rate models are commonly used to model the term structure of interest rates. These include market models, used extensively in the money markets; affine term structure models, widely used in the bond markets; and Heath, Jarrow, and Morton (HJM) models, less widely used but applied to both the bond and money markets.
Exposure Rating
Authors: Mata Ana J. 
Published: September 2014

Abstract
The exposure rating method is a rating method used in reinsurance to estimate the total expected losses for the layer covered. The exposure method uses the loss experience of a similar portfolio of policies by means of a claim size distribution in order to estimate the expected losses of the portfolio of policies to be covered. This method is often used as a benchmark when there is no sufficient credible individual claim history for the specific risk.
K
endall,
M
aurice
G
eorge
Authors: Altman Douglas G. 
Published: September 2014

Abstract
Kendall (1907–1983) worked for the British Chamber of Shipping, then joined the London School of Economics, later directing a computer consultancy company, and then the World Fertility Survey. He revised Yule's classic textbook, then wrote the first comprehensive and advanced book on statistics. Many other books followed, as he did research in many areas including rank correlation, time series, and sample surveys.
Tukey,JohnWilder
Authors: Kafadar Karen 
Published: September 2014

AbstractJohn Tukey (1915–2000), is best known to statisticians for founding the field of exploratory data analysis, for introducing the jackknife as a tool for characterizing the uncertainty in a statistic, and for guiding and contributing to research in robust methods. His work in other fields included Tukey's lemma; fast fourier transform; and coining the word “bit” for computer science.
Partner Study
Authors: Shiboski S. C. 
Published: September 2014

AbstractPartner studies are epidemiologic investigations of transmission of infectious diseases in susceptible partners of known infected individuals. Widely used for studying transmission of sexually transmitted diseases (STD) such as Human Immunodeficiency Virus (HIV), these studies are applicable in situations where “partnerships” which link known susceptible and infectious individuals can be identified. Eligibility typically requires that exposure to infection in susceptible individuals is limited to a previously infected partner. This allows potential bias associated with uncertainty about the source of infection to be reduced or eliminated. Partner studies focus primarily on the influence of measured covariates on the probability of transmission, but can also be used to gain knowledge of key parameters such as the infectivity (transmission risk per unit of exposure). Statistical analyses of data from these studies provide a number of unique challenges, most of which arise from the incomplete nature of data describing exposure and infection.
Analytic Heirarchy Process
Authors: Saaty Thomas L. 
Published: September 2014

Abstract
The Analytic Hierarchy Process (AHP) is a theory of relative measurement of intangible criteria. With this approach to relative measurement, a scale of priorities is derived from pairwise comparison measurements only after the elements to be measured are known. The ability to do pairwise comparisons is our biological heritage and we need it to cope with a world where everything is relative and constantly changing and thus, there are no fixed standards to measure things on. In traditional measurement, one has a scale that one applies to measure any element that comes along that has the property the scale is for, and the elements are measured one by one, not by comparing them with each other. In the AHP, paired comparisons are made with judgments using numerical values taken from the AHP absolute fundamental scale of 1 to 9. A scale of relative values is derived from all these paired comparisons and it also belongs to an absolute scale that is invariant under the identity transformation like the system of real numbers. The AHP is useful for making multicriteria decisions involving benefits, opportunities, costs, and risks. The ideas are developed in stages and illustrated with examples of real‐life decisions. The subject is transparent and easy to understand why it is done the way it is along the lines discussed here. The AHP has a generalization to dependence and feedback; the Analytic Network Process (ANP) is not discussed here.
Relationship Testing
Authors: Epstein Michael P. , Boehnke Michael 
Published: September 2014

Abstract
Linkage analyses of genetic diseases are accomplished using data from related individuals in families. These analyses assume that one correctly specifies the relationships among all pairs of relatives within these different families. Relationship misclassification may lead to inappropriately reduced or increased evidence for linkage. Sometimes, one can identify misspecified relationships by detecting Mendelian inconsistencies. However, for smaller family subsets such as sibling pairs, it may not be possible to identify misspecified relationships with certainty. Still, with an adequate amount of marker data, one may be able to identify misspecified relationships probabilistically. This section describes a variety of different statistical methods that one can apply to identify misspecified relationships of relative pairs. Generally, these methods are accurate and computationally fast.
Calibration
Authors: Lee J. Jack , Moon Hojin 
Published: September 2014

AbstractStatistical calibration is often used in biomedicine as a means to estimate the quantity of a substance that is difficult or expensive to measure by another measurement, which is easier or cheaper to obtain. Classical or inverse estimators can be computed from parametric or nonparametric regression models. Multivariate calibration, Bayesian modeling, and optimal designs are reviewed. Selected topics such as interval estimation, measurement error, robustness, heteroscedasticity, instrumental calibration, and survey sampling are discussed as well.
Serial Dilution Assay
Authors: Ridout Martin 
Published: September 2014

AbstractSerial dilution assays are used to estimate concentrations of bacteria in samples of food, water, soil, and so on. Key assumptions are that the bacteria are distributed randomly throughout the medium, and that the presence or absence of bacteria in subsamples can be ascertained reliably. The concentration of bacteria can then be estimated based on the presence or absence of bacteria in subsamples of different volumes. Essentially, the same method can be used to estimate the proportion of individuals in a population that have some characteristic, for example, in immunology, the proportion of cells that are immunocompetent. The term limiting dilution assay is often used for this type of application. This article considers point and interval estimation of concentration, including bias correction, tests of the underlying assumptions, and issues of experimental design for dilution assays.
O
blimin Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Oblimin rotation
is a general form for obtaining oblique rotations used to transform vectors associated with principal component analysis or factor analysis to
simple structure
. Oblimin is similar to the
Orthomax rotation
procedures used in orthogonal rotation in that it, too, includes an arbitrary constant used to obtain different rotational properties. A numerical example is included. While most orthogonal rotations use some form of Orthomax rotation, this is no longer the case with Oblimin rotation for the oblique case.
Orthoblique Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Orthoblique rotation
is a two‐stage procedure for obtaining
oblique rotations
used to transform vectors associated with
principal component analysis
or
factor analysis
to
simple structure
. The first stage will usually be an orthogonal rotation such as
Varimax
. The nature of the second stage will depend to some extent on the degree of obliqueness desired. A numerical example is included with two second‐stage options. This technique is sometimes referred to as the
Harris–Kaiser rotation
.
Varimax Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Varimax rotation
is the most common form of
orthogonal rotation
used to transform vectors associated with
principal component analysis
or
factor analysis
to
simple structure
. It is a special case of
orthomax rotation
, which maximizes the sums of squares of the coefficients within each of the resultant vectors as opposed to
Quartimax
, which maximizes the sums of squares of the coefficients across the vectors for each of the original variables. A numerical example is included.
Multistage Sampling
Authors: Shimizu Iris 
Published: September 2014

Abstract
Multistage sampling is an extension of cluster sampling in which sampling is done in stages with smaller units being defined and selected at each stage within the units selected at the prior stage. Multistage sampling is used to reduce data collection costs. However, its use usually results in larger sampling errors than would result from simple random samples of the same size. The article illustrates how to apply multistage sampling in terms of two‐stage samples. Formulas are given for calculating estimates and their variances for aggregates and ratios under different situations. Procedures for determining optimum sample sizes and allocation between primary and secondary sampling stages are discussed. Some methods are also given for controlling the effects of variation in sizes of primary sampling units on sampling errors.
Foundations of Probability
Authors: Lindley Dennis V. 
Published: September 2014

AbstractProbability, as a mathematical subject, is essentially unique and its formal rules are described. There are many different interpretations, of which three are mentioned: classical, frequentist, and in terms of belief. The many and varied justifications for thinking that beliefs should conform to the rules of probability, the Bayesian view, are described. The connections with decision‐making, the concept of utility, and the role of subjectivity are discussed. It is pointed out that the frequentist and Bayesian interpretations lead to distinct, practical implications of probability, especially in connection with the likelihood principle, and illustrations are provided. The role of exchangeability, which links the two views, is developed. Alternatives to probability are briefly mentioned.
Mean Square Error
Authors: Schluchter Mark D. 
Published: September 2014

Abstract

If is an estimator of a parameter θ, then the mean‐square(d) error (MSE) of the estimator is defined as the expectation of the squared deviation of from θ: . The MSE is a measure of closeness of the estimator to the true value. It can be shown that

That is, MSE reflects both the bias (accuracy) of the estimator, that is, how much its expected value deviates systematically from the true value, as well as the variance (precision) of the estimator, which indicates how much it varies about its expected value due to sampling variability.
Bayesian Item Response Theory Estimation
Authors: Swaminathan Hariharan 
Published: September 2014

AbstractThe problem of fundamental importance in item response theory is the accurate estimation of the parameters that characterize the items and the ability or the proficiency levels of examinees. While in general, likelihood‐based approaches such as joint, conditional, and marginal maximum likelihood procedures provide reasonable estimates of the item parameters, more often than not these procedures yield estimates in the two‐ and three‐parameter models that are inadmissible in the sense that the estimates of the ability and item parameters fall outside acceptable ranges. Bayesian approaches, by taking into account prior and collateral information, overcome the problems encountered by likelihood‐based approaches.
Exact Methods for Categorical Data
Authors: Hershberger Scott L. 
Published: September 2014

Abstract
Exact tests evaluate the significance of a test statistic by using the test statistic's empirically derived sampling distribution instead of a theoretical sampling distribution. Preference for exact tests may occur in situations in which the sample size is small, or when the data have distributional deficiencies. Two well‐known exact tests, Pitman's test and Fisher's test, are described.
Rater Bias Models
Authors: Saudino Kimberly J. 
Published: September 2014

Abstract
Raters' response tendencies, standards, or expectations can result in the tendency to consistently overestimate or underestimate the behavior of targets. Such rater biases can influence the outcomes of behavioral genetic studies. Quantitative genetic models allow researchers to determine the significance of rater biases and estimate genetic and environmental influences on behaviors controlling for bias.
Scales of Measurement
Authors: Wuensch Karl L. 
Published: September 2014

Abstract
Scales of measurement, as conceived by S. S. Stevens, are defined with respect to (1) the extent to which empirical relationships among the measured objects correspond to numerical relationships among the measurements, and (2) the types of transformations that can be applied to the scale without distorting the structure of the scale. Stevens argued that whether or not a particular statistical analysis is ‘permissible’ depends upon the scale of measurement of the data being analyzed. While this assertion has been defended by some statisticians, it has been attacked by others. Many statistical textbooks for social scientists are organized around the assertion that scale of measurement is a critical factor in determining which statistical analysis is appropriate. Here, the authors argue that parametric statistical methods are appropriate only for interval level data and that nonparametric methods should be employed with noninterval data. One's attitude about the relationship between scale of measurement and choice of statistical analysis may follow from one's more basic ideas about measurement and the nature of reality. Ultimately, the distinction between interval and ordinal scales depends on whether one believes the relationship between the measurements and the true amounts of the measured attribute is positive linear (interval) or merely positive monotonic (ordinal), and such beliefs are largely a matter of faith.
Multitrait–Multimethod Analyses
Authors: Pardy Susan A. , Fabrigar Leandre R. , Visser Penny S. 
Published: September 2014

Abstract
The multitrait‐multimethod (MTMM) framework originally proposed by Campbell and Fiske has long guided efforts to assess the discriminant and convergent validity of measures. Numerous statistical approaches for analyzing MTMM matrices have been proposed. Surprisingly, little consensus has developed on the most appropriate method for analyzing these matrices or what criteria should be used as a basis for choosing the most appropriate method. We provide a nontechnical review of statistical approaches for analyzing MTMM matrices. We conclude with a set of general recommendations regarding the analysis of MTMM data.
Clinical Trials and Intervention Studies
Authors: Lesaffre Emmanuel , Verbeke Geert 
Published: September 2014

Abstract
Intervention studies are experimental studies that evaluate the effect of an intervention on humans. There are many different types of intervention, including medical, social, psychological, and so on. In all of these cases, interventions seek to improve the condition of the individual. In an epidemiological context, an intervention study is a specific type of a cohort study. When a medical intervention is involved, one speaks of a clinical trial. Clinical trials take a prominent place in clinical research, as they are pivotal for the registration of new medical treatments.
We give a brief overview of the types of intervention studies, but special attention will be paid to clinical trials. Aspects of designing and analyzing an intervention study will be presented.
Logistic Regression in Practice
Authors: Lemeshow Stanley , W. Hosmer David 
Published: September 2014

Abstract
This article deals with the use of logistic regression to model data, where the response variable is binary and there are potentially numerous explanatory or predictor variables. Methods of estimating the coefficients in the model and procedures for hypothesis testing are considered. The correspondence of the coefficients of the logistic regression model to the odds ratio is noted along with methods for computing point estimates and confidence intervals for the odds ratio. Interpretation of coefficients for dichotomous, polytomous, and continuous independent variables is carefully developed. Confounding and effect modification are easily dealt with through the use of this modeling technique. All these concepts are illustrated through the analysis of low birth weight data.
Repository
Authors: Winkler Cheryl A , Groover Kathleen H. 
Published: September 2014

AbstractRepositories for the acquisition, storage/archival, and distribution of biosamples are essential to the success of clinical trials and biomedical research. In the current era of genomics and proteomics, the banking of human biosamples and associated phenotypes for the discovery of genetic factors underpinning rare and common diseases has expanded the role and impact of repositories and biobanks. Today's repositories are banking an unprecedented number of biosamples and data for molecular‐based research. This article provides an overview of repository models, the core function and management of repositories, and issues that pertain to the protection of donor rights and confidentiality.