bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonparametric Curve Estimator
Authors: Tarter Michael E. 
Published: September 2014

AbstractThe roles that nonparametric curve estimators can play are illustrated in studies of polluting chemicals that have built up in the environment and of crop forecasting techniques. The multivariate normal model and model‐free curve estimator representations are compared. Equations and approaches are described that enable researchers to input prior information that divides variates into two categories, variates of primary importance and variates that are nuisances in the sense that their detained distributional structure is relevant solely insofar as its effects on variates of primary importance. It is shown that conditional density estimators can be computed given user‐selected values of nuisance variates, not computed as a second step that follows a first step, which requires the estimation of a high‐dimensional joint distribution. It is argued that this approach resembles parametric methodology that has long been fruitfully employed in the field of experimental design.
Inverse Prediction
Authors: Brown Philip J. 
Published: September 2014

AbstractInverse prediction concerns predicting one set of measurements from another set when the direction of causation and possibly error operates in the opposite direction. An example is that of calibration in which it is desired to use a cheap and quick but error‐prone measurementY, to predict the true amount of a constituentx, which in itself can be measured accurately in laboratory conditions but at much greater effort or cost. After taking data on samples using both measurements (the training or learning data) it is desired to use the cheap measurement in future.
Logarithmic Regression
Authors: El‐shaarawi Abdel , Viveros(‐aguilera) Román 
Published: September 2014

AbstractLog regression (LR) is widely used to model environmental data because the measurements tend to show positively skewed frequency curves and higher variability with higher concentration levels. A compelling reason for the popularity of LR is the simplicity of estimating and making inferences about the regression coefficients using existing software packages for the normal theory linear models.
Multicollinearity
Authors: Wakefield Jon 
Published: September 2014

Abstract
Multicollinearity is said to occur when at least one column of the design matrix is linearly dependent on the other columns, and can cause problems of both interpretation and computation in regression analysis. The problem manifests itself when a regressor is highly correlated with another regressor, or a linear combination of a number of regressors. This does not guarantee multicollinearity, however, since two vectors may have correlation one, but be orthogonal. Sometimes this phenomenon is referred to as collinearity but, in this article, this term is reserved for the situation in which several points lie on a line.
Nonlinear Regression‐Extensions
Authors: Smyth Gordon K. 
Published: September 2014

AbstractThe basic idea of nonlinear regression is the same as that of linear regression, namely to relate a response to a vector of predictor variables. Nonlinear regression is characterized by the fact that the prediction equation depends nonlinearly on one or more unknown parameters. Whereas linear regression is often used for building a purely empirical model, nonlinear regression usually arises when there are physical reasons for believing that the relationship between the response and the predictors follows a particular functional form. The unknown parameters in the regression function are typically estimated by minimizing the residual sum of squares, although other criteria can be used if it is desired to protect against non‐normal errors. Popular nonlinear regression models include sums of exponential functions, rational functions or growth curves such as logistic or Gompertz curves. The estimates usually have to be computed iteratively by using numerical optimization methods such as the Gauss–Newton algorithm. Good‐quality algorithms usually incorporate step‐length modifications designed to ensure a reduction in the residual sum of squares at each step. In some cases, the complexity of the fitting process can be reduced by taking advantage of conditionally linear parameters. Inference for nonlinear regression is classically based on analogies with linear regression using the gradient matrix from the Gauss–Newton algorithm, but recent work on curvature measures has led to new diagnostics and higher‐order approximations.
Quantile Regression
Authors: Koenker Roger 
Published: September 2014

AbstractClassical least squares regression may be viewed as a natural way of extending the idea of estimating an unconditional mean parameter to the problem of estimating conditional meanfunctions; the crucial link is the formulation of an optimization problem that encompasses both problems. Likewise, quantile regression offers an extension of univariate quantile estimation to estimation of conditional quantile functions via an optimization of a piecewise linear objective function in the residuals. Median regression minimizes the sum of absolute residuals, an idea introduced by Boscovich in the eighteenth century, and developed by Edgeworth in the nineteenth century.The asymptotic theory of quantile regression closely parallels the theory of the univariate sample quantiles. Computation of quantile regression estimators may be formulated as a linear programming problem and efficiently solved by simplex or barrier methods. A close link to rank‐based inference has been forged from the theory of the dual regression quantile process, or regression rankscore process. Recent work has extended quantile regression into time‐series, spatial models, survival analysis, and nonparametric estimation.
Regression Diagnostics
Authors: Davison Anthony C. 
Published: September 2014

AbstractStatistical diagnostics are used to help data analysts detect and find the source of any problems that may have arisen with the application of statistical procedures, and if possible to suggest remedies. Often they have two elements: a graph constructed to highlight potential difficulties and a numerical test statistic intended to help gauge their significance. Regression involves studying the dependence of one or a few variables on others and is perhaps the most widespread statistical activity. Consequently, regression diagnostics have been well studied and are incorporated into most statistical packages. Moreover, the general ideas that underlie them are useful much more widely, with close links togoodness of fitandrobustness.
Variable Selection via Regularization
Authors: Li Runze , Cui Hengjian 
Published: September 2014

AbstractVariable selection has been an active research topic since the 1970s. There are hundreds of publications on variable selection. This entry describes regularization methods for variable selection developed in the recent literature. The regularization methods include classical variable selection criteria such as AIC and BIC and modern variable selection approaches such as LASSO, SCAD, and LARS.
Composite Sampling
Authors: Patil G.P. 
Published: September 2014

Abstract
The high costs of laboratory analytical procedures frequently strain environmental and public health budgets. Whether soil, water, or biological tissue is being analyzed, the cost of testing for chemical and pathogenic contaminants can be quite prohibitive. Composite sampling can substantially reduce analytical costs because the number of required analyses is reduced by compositing several samples into one and analyzing the composited sample. By appropriate selection of the composite sample size and retesting of select individual samples, composite sampling may reveal the same information as would otherwise require many more analyses. Many of the limitations of composite sampling have been overcome by recent research, thus providing more widespread potential for the use of composite sampling to reduce costs of environmental and public health assessments, while maintaining and often increasing the precision of sample‐based inference.
Hansen‐Hurwitz Estimator
Authors: Christman Mary C. 
Published: September 2014

AbstractThe Hansen–Hurwitz estimator is a design‐based procedure for estimating population totals or means when sampling is with replacement. Its strength lies in its approach incorporating unequal sampling probabilities to provide an estimator that is more precise than many alternative estimators. It is commonly used for cluster sampling, stratified probability sampling, and adaptive sampling designs. A very common use of the estimator is for cluster sampling designs in which clusters are selected proportional to size.
Model‐Based Inference
Authors: Chambers Ray 
Published: September 2014

AbstractA basic problem in survey sampling is that of using the sample data for a survey variableYto infer the value of the totalTofYover the sampled population. The model‐based approach to this problem is to base this inference on a joint statistical model for both the population values ofYand, where appropriate, for the sampling method. This is in contrast to design‐based sampling inference, where the only source of uncertainty is the outcome of the sampling method.
Randomized Branch Sampling
Authors: Valentine Harry T. 
Published: September 2014

Abstract
Randomized branch sampling (RBS) is a special application of multistage probability sampling, which was developed originally by Jessen to estimate fruit counts on individual orchard trees. In general, the method can be used to obtain estimates of many different attributes of trees or other branched plants. The usual objective of RBS is the estimation of a total quantity for a tree or a branch, for example, woody dry matter or volume, leaf dry matter, or counts of tree‐inhabiting insects. RBS also has potential application in the estimation of attributes of river networks and other branched structures.
Sampling Agricultural Resources
Authors: Nusser Sarah M. , Zimmer Stephanie A. 
Published: September 2014

Abstract
Agricultural surveys gather data about agricultural production, land management practices, and economic outcomes for farms. Because they involve a complex mix of estimation goals and reporting units, probability samples for agricultural surveys are challenging to design. Two types of sampling frames are used: list frames of agricultural producers and area frames that represent units of land. List frames are cheaper to maintain and easier to select samples from, but may be incomplete. Area frames, which involve selecting land areas or points on the land, are expensive to use, but have full coverage of the population. Some surveys use both types of frames to control costs and estimate coverage bias of the list frame. Regardless of the frame, stratification and multistage sampling are commonly used to balance operational constraints and precision goals for agricultural surveys.
Added Risk
Authors: West R. Webster 
Published: September 2014

Abstract

Dose–response studies of environmental toxins are designed to determine the detrimental effects associated with exposure to the toxin, over a particular range of exposures. To properly quantify the toxic effect of a toxin, the
background risk
of no exposure to the toxin, R(0), must first be specified. The additional risk incurred by exposure to a dose,
d
, of the toxin is then called the added risk (AR) at this dose and is given by AR(
d
) = R(
d
) – R(0).
Ames Assay
Authors: Margolin Barry H. , Kim Byung‐Soo 
Published: September 2014

Abstract

Who would have thought that
Salmonella
bacteria, the very same strains that are the bane of travelers, would also contribute to human health via their use in bioassays for genetic mutation? Developed by Ames and colleagues in the early 1970s, the Ames assay continues to hold a preeminent position in genetic toxicology. Its applications have grown increasingly diverse, ranging from attempts at cancer prediction to screening of industrial byproducts for possible mutagens. The assay is currently conducted in thousands of laboratories worldwide. The Ames assay has produced a wealth of experimental data over the last two decades. During the same period many authors have proposed mechanistic models for the analysis of Ames data. Much of this modeling, based on radiation ‘hit theory’, derives from the work of Haynes and Eckardt.
Development Toxicity Study
Authors: Regan Meredith M. 
Published: September 2014

Abstract
Development toxicology investigates the impacts on human development of exposure to potentially toxic substances or conditions. Controlled development toxicity studies in laboratory animals play an important role in the toxicological assessment of foods, drugs and chemicals. In contrast to carcinogenicity studies, methods to extrapolate safe exposure levels from animals to humans are not well developed. The mechanisms of most development toxicants are unknown, and statistical methods for carcinogenicity do not directly apply since the outcomes and the experimental design differ. Manifestations of development toxicity include death, malformations, growth alterations, and functional impairment. Improving methods of quantitative assessment of adverse development effects is a current research focus.
Disease Clustering
Authors: Elliott Paul 
Published: September 2014

Abstract

In recent years there has been heightened awareness about the possible effects of environmental pollution on health, reflecting an increasing public expectation of a clean and healthy environment. The public and media alike have focused on the possible risks to individuals and their communities from local sources of industrial pollution. These concerns have to some extent been fueled by scientific uncertainties about the health effects of low levels of pollution. All these issues tend to come to the fore when disease excesses (
clusters
) in the local community are reported or suspected. This article reviews some of the issues affecting the analysis and interpretation of clusters of disease, including examples where evidence of local disease excess is derived from a disease mapping exercise or a point source investigation.
Disease Registers: Basic
Authors: Darnell Ross 
Published: September 2014

Abstract
A disease registry defines a process that manages registers, which comprise cases of a particular disease that have occurred in a population. The registry may include demographic information about the cases (e.g., age, gender, and place of residence), dates of disease diagnosis, and perhaps additional information such as dates of death, or exposure and risk factor information. Registers are multipurpose being used to improve patient care, reviewing of patients with chronic illness, public health reporting, surveillance of diseases within a population, planning and provision of health care, and research. There is a tension between the increasing demands for personal autonomy and the need for society to recover as much information as possible to learn how best to control disease. There is a real need for maintaining confidentiality of individual health data and for informed consent before these data are used for research.
Dose Calibration
Authors: Davidian Marie 
Published: September 2014

Abstract
Calibration involves determination of an unknown level (dose, concentration) of an antigen, which may be an enzyme, hormone, protein, chemical compound, or other substances in a test sample based on observation of a (usually) quantitative measurement derived from an assay technique, such as an optical density (enzyme‐linked immunoassay) or gamma count (radioimmunoassay). This procedure is commonly used in environmental health studies where levels of allergens, exposure agents, and other substances are ascertained and related to health outcomes. The statistical framework underlying calibration, including development of the so‐called standard curve from which unknown levels may be calibrated, assessment of the precision with which calibration is performed, and evaluation of assay quality and consistency, is discussed. Copyright © 2015 John Wiley & Sons, Ltd.
Extrapolation, Risk‐Analytic
Authors: Hertzberg Richard C. 
Published: September 2014

Abstract
Quantitative extrapolation refers to a process where inferences about data‐poor scenarios are developed from studies of data‐rich scenarios. For dose–response relationships in environmental risk assessment, several different kinds of extrapolation are used in order to accommodate the variety of available information and risk assessment goals. Examples of such extrapolation include estimating human health risk at low doses from rodent toxicology data at high doses, or estimating ecosystem response at a given ambient pollution concentration from laboratory studies of individual animal or plant species at similar concentrations. The examples discussed in this article are for human health risk assessment but most of the principles apply to ecological risk assessment as well. These examples include low‐dose extrapolation of cancer response (extending a fitted function below the range of the data), simple scaling methods (e.g. allometric interspecies extrapolation), biomathematical models (e.g. physiologically‐based pharmacokinetic models), and quantitative formulas for estimating mixture response (e.g. modifying dose addition to account for synergism).
Familial Aggregation
Authors: Seillier‐moiseiwitsch Françoise 
Published: September 2014

AbstractBy familial aggregation is meant the occurrence of a disorder at a higher frequency in the relatives of affected individuals than in the general population. This aggregation may be due to genetic, cultural or environmental factors. Assessing whether diseases cluster in families, and searching for the explanation when they do so, are the main questions genetic epidemiologists address.
Heritability
Authors: Fry James D. 
Published: September 2014

Abstract
The heritability of a trait is the proportion of variation in a population that can be attributed to genetic differences among individuals. Although usually applied to continuously varying traits, such as milk yield in dairy cows or blood pressure in humans, the concept of heritability can also be applied to discontinuous traits that have complex patterns of inheritance, such as the presence or the absence of medical conditions such as diabetes. The heritability can be estimated by measuring the degree of resemblance, quantified by the covariance, between relatives of various types, although special care must be taken when estimating heritability in humans.
Hormesis
Authors: Calabrese Edward J. 
Published: September 2014

Abstract

This article provides a general description of the hormesis dose–response model. Hormetic dose–responses have become reported more frequently in the literature with advances in measuring chemicals at progressively lower doses and with the extensive use of
in vitro
methods, which permits an evaluation of more doses/concentrations that has been typically used in
in vivo
studies. It is expected that the hormesis concept will play a progressively more significant role in the hazard assessment process and in drug discovery and development in forthcoming years.
Interlaboratory Studies
Authors: Vangel Mark G. 
Published: September 2014

Abstract

Interlaboratory studies (sometimes referred to as
collaborative studies
, or
round robins
) are investigations in which measurements are made on one or several materials at several laboratories. These studies are typically conducted for one of three reasons: (i) to evaluate a measurement method; (ii) to evaluate a reference material; or (iii) to evaluate the proficiency of the laboratories themselves.
Occupational Mortality
Authors: Symons Michael J. 
Published: September 2014

AbstractIn studies of mortality of industrial workers, statistical options are limited when: (1) only a few deaths are observed; (2) exposure information at the workplace is nonexistent; (3) personal concomitant exposure data are not available. With none, some, or all of these limitations, the standardized mortality ratio (SMR) is the most frequently used statistic for occupational and environmental cohorts. The SMR is the total number of observed events,O, divided by the total number expected events,E, for those under study. The SMR, as a one‐dimensional summary statistic, is both attractive and limited in scope. It is widely used in mortality studies of industrial populations, serving to screen for excess causes of death and to test for an excess of a specific cause of mortality. A variety of study designs – commonly, proportional mortality, cohort mortality, and case‐control studies – are utilized in the further investigation of hypotheses generated or for more confirmatory tests of results from previous analyses.
Outcome‐Dependent Selection Models
Authors: Ding Jieli , Lu Tsui‐shan Eva , Zhou Haibo 
Published: September 2014

Abstract
An outcome‐dependent selection (ODS) is a retrospective sampling scheme where one observes the environmental exposure/covariates with a probability that depends on the observed outcome values. By allowing the selection probability of each individual to depend on the outcome variable, ODS design can reduce the cost of the study and improve the efficiency. The case‐control study and case‐cohort study in epidemiology are well‐known examples of ODS schemes. We review recent advances in research on ODS designs with a continuous response. This includes inferences on more sophisticated designs with survival data, clustered/longitudinal data, two‐stage ODS design, and outcome and auxiliary‐dependent (OADS) schemes.
Quantitative Structure‐Activity Relationships (
QSAR
)
Authors: Williams Paige L. 
Published: September 2014

Abstract
Quantitative structure activity relationships (QSARs) have historically been used in the design and evaluation of new pharmaceutical products. With the rapid increase in modern computing power, the use of QSAR methods in the field of drug development has seen an explosive range of applications in the late 1990s. QSAR approaches have also been used in environmental risk assessment to predict the carcinogenicity or genotoxicity of toxic agents, and thus reduce the costs of conducting long‐term animal studies. The application of QSAR methods has traditionally encompassed two different approaches: the extrathermodynamic approach proposed by Hansch and Fujita, and the ‘substructural’ or ‘connectivity’ approach of Free and Wilson.
Repeated Measures
Authors: Kenward Michael G. 
Published: September 2014

Abstract

A repeated measures study is distinguished by the collection of a sequence of measurements from each unit over a period of time. Repeated measures can be collected in both experimental and observational settings; examples of the latter are often referred to as longitudinal studies. The sequence of measurements from one unit is sometimes called the unit's
profile
. These profiles form a collection of short time series, and one should expect the measurements from the same unit to be mutually correlated.
Reproductive Epidemiology
Authors: Cho Sung‐Il , Wang Xiaobin 
Published: September 2014

Abstract
A widely accepted definition of epidemiology is ‘the study of the distribution and determinants of health‐related states or events in specified populations, and the application of this study to control of health problems’. Reproductive epidemiology focuses on the events in various stages of the human reproductive process, including sperm production, menstrual cycling, ovulation, sexual interaction, conception, fetal growth, and growth and development of the infant.