bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ranks
Authors: Hallin Marc 
Published: September 2014

Abstract
Rank tests were introduced as a reaction against the traditional assumption of Gaussian observations. The most attractive feature of ranks and rank statistics is that they are distribution‐free, and thus, in principle, allow for exact critical values, irrespective of the underlying distribution of the observation. Their domain of application is, mainly but not exclusively, the class of semiparametric models obtained from the traditional parametric ones (location, scale, regression, autoregression, etc.) by treating the density of the underlying error or noise as an unspecified nuisance. Their popularity is explained by their success in a variety of practical situations, and their robustness against deviations from standard distributional assumptions.
Lasso, the
Authors: Knight Keith 
Published: September 2014

AbstractThe LASSO (least absolute shrinkage and selection operator) is a method of estimation in linear regression (and related) models that combines shrinkage estimation achieved in ridge regression with model selection where some (or all) parameter estimates are set to zero. The LASSO is capable of producing a “sparse” estimate of the parameter and can be used effectively even when the number of parameters exceeds the number of observations. LASSO estimates can be computed very efficiently using a number of methods, including coordinate descent methods. A number of generalizations of the LASSO have been proposed, including the fused LASSO, the group LASSO, and the adaptive LASSO.
Multivariate Adaptive Regression Splines
Authors: Kooperberg Charles 
Published: September 2014

Abstract
The multivariate adaptive regression splines (MARS) algorithm was developed by Friedman as a nonparametric method for multiple regression. The MARS algorithm uses adaptively selected spline functions. Because of its adaptivity MARS is often able to model functions that express high variation in one region of the predictor space and are smoother in other parts.
Line‐Transect Sampling, New Approaches
Authors: Melville Gavin , Cowling Ann 
Published: September 2014

Abstract
The widespread use of line‐transect surveys to estimate the abundance of ecological populations has seen a number of new developments in methodology. For example, nonparametric approaches have been introduced to estimate detection probabilities for objects in the target population. Also line‐transect data have been increasingly used to study a range of spatial models that may be of interest to ecologists. The treatment of measurement errors and recent sampling innovations such as sequential and adaptive sampling have increased the flexibility of the line‐transect approach and provided more robust estimates of abundance. Debate over an appropriate frame of inference, specifically design‐based versus model‐based, is a motivation for alternative estimation techniques such as mark–recapture and the use of Bayesian statistics. This article summarizes some of the recent developments in line‐transect sampling.
Multistage Design
Authors: Christman Mary C. 
Published: September 2014

Abstract
Multistage sampling designs are probability based sampling strategies for obtaining more information about a population with less cost than would be possible under a single stage design. Examples of such approaches include ranked set sampling, double sampling for stratification, and multistage cluster sampling. In all cases a first stage sample is selected, e.g. a set of randomly selected transects through a habitat, and subsequently a subset of observations from this initial sample, e.g. observations at randomly selected locations along the transects, are selected for data collection.
Age‐Growth Modeling
Authors: Nokoe K. Sagary 
Published: September 2014

AbstractThis section is on growth of nonhuman individuals. Growth of an individual or a group of organisms is generally defined as the change in dimensions over time. Growth rates vary between and within plant and animal species. They are generally influenced by both genetic and phenotypic factors, and may be negative or positive. Positive growth relates to increases in dimensions (weight, height, and number of component parts) over time, whereas decay or negative growth refers to the decline over time. Human interference, either intentionally or accidentally, environmental influences, and management practices can substantially influence the growth forms and patterns of individuals and population. Age‐growth functions constitute important analytical tools in the management of natural resources. These models are explicit expressions of both theory and empirical knowledge, and are useful for growth and yield prediction and evaluation of alternative management strategies, among others. The approach to age‐growth modeling may be based on either primary (single) unit or stand (aggregate of units). Irrespective of modeling philosophy, the goal is identification of the most parsimonious and biologically reasonable model that best describes the relationship between the response variable (which is a measure of growth) and time (age).
CorrelatedMultinomialData
Authors: Zhu Yiliang 
Published: September 2014

AbstractCorrelated multinomial data, including correlated binomial, arise from clustered sampling schemes such as grouped sampling or repeated measurements. Correlation induced by clustering can be of interest in its own right, but must be accounted for in statistical analyses to avoid inaccurate or inefficient inference. This article discusses common approaches to the treatment of correlated multinomial data and illustrates dose‐response modeling via generalized estimating equations using an example from developmental toxicity experiment.
Databases, Health Assessment
Authors: Darnell Ross 
Published: September 2014

Abstract
Environmental issues regarding possible environmental pollution links to human and ecological health are becoming more numerous and controversial. The ideal approach to assessing potential health risks from environmental exposure is to conduct well‐designed, prospective, environmental, epidemiological studies. However, prospective epidemiological studies are difficult and expensive to undertake, and even retrospective studies can be prohibitively complex. Reliable environmental data are not easy to find and often they may not have been collected in a manner that makes them easy to use. The availability of data increases the risk of identifying single units using increasingly powerful search engines by ill‐intentioned users. The increasing public availability of health and environmental data raises the risk of identifying individuals, forcing data owners to develop techniques to reduce this risk.
Dose‐Response Model
Authors: Gaylor David 
Published: September 2014

Abstract

A measurement,
Y
, of an effect can be represented by
Y
= E(
Y
) + error. E(
Y
) is the expected (true) value of an effect and error is the deviation of the measurement from its true value due to inherent measurement error and/or experimental variation. A dose–response model is a mathematical function
f
that relates the expected value, E(
Y
), of some measurable effect,
Y
, to a dose,
d
, of a substance E(
Y
) =
f
(
d
).
Epidemic Models
Authors: Isham Valerie 
Published: September 2014

AbstractMathematical modeling of infectious diseases has a long and distinguished history and is one of the most successsful areas of mathematical biology. Understanding the dynamics of the spread of an infection brings possibilities for its control. Human infections such as influenza, malaria, and HIV are still major worldwide causes of morbidity and mortality. In 2003, a new infection, SARS, spread rapidly around the world, with considerable transmission taking place before the infection was even recognized. In such situations, a large‐scale epidemic can only be avoided if strict control strategies are imposed quickly. A clear understanding of the basic theory of epidemic models is a prerequisite for this and, equally, for informing the development of the successful vaccination policies against a wide range of infections in routine use around the world. This article provides a brief introduction to that theory and to some natural extensions of the simple models and the practical concerns that they are designed to address.
Healthy Worker Effect
Authors: Eisen Ellen A. , Picciotto Sally , Robins James M. 
Published: September 2014

Abstract
Epidemiologic studies of occupationally exposed cohorts are useful in identifying and quantifying environmental risks because workers are generally exposed to higher levels of toxic materials than the general population. Although higher exposures make it easier to detect and characterize modest elevations in risk, such studies are plagued by a particularly pervasive form of selection bias, referred to as the healthy worker effect (HWE). The HWE is reflected in better health status of exposed workers relative to the general population. This bias arises from two sources: (i) workers were initially healthy enough to be hired, whereas the general population includes persons unfit for work, and (ii) workers who stay healthy (survivors) remain exposed, whereas less healthy coworkers reduce exposure via time off work, job transfer, or early termination. Unlike standard methods, G‐estimation has been shown to reduce healthy worker survivor bias.
Litter Effect
Authors: Kupper Lawrence L. 
Published: September 2014

AbstractIt has long been recognized that humans exposed to certain drugs, chemicals, and other environmental hazards may be at increased risk of various health problems. Exposure‐related, adverse, heritable, human health outcomes currently receiving increased attention by public health professionals are fertility and pregnancy complications, birth defects, and developmental abnormalities. Well‐conducted environmental epidemiology studies on human populations would provide the most relevant data for quantifying relationships between exposure levels and adverse pregnancy‐ related outcomes. However, useful information from observational data on humans is often limited in both quantity and quality. So, despite the obvious species‐to‐species extrapolation problem, controlled laboratory experiments on rodents are commonly conducted to provide developmental toxicology data. This article focuses specifically on developmental toxicology studies, where pregnant rodents are exposed to a suspected harmful agent during the period of major fetal organogenesis and structural development. It is well known that an inherent characteristic of data from such rodent teratology studies is the so‐calledlitter effect, i.e. the tendency for littermates all to respond in the same way to stimuli, unlike animals from different litters. Litter effects seem to be present in almost all developmental toxicology studies, and data analyses that do not appropriately account for these litter effects can lead to erroneous statistical inferences.
Longitudinal Studies
Authors: Dean C. B. , Hu X. J. 
Published: September 2014

AbstractA longitudinal study observes its subjects repeatedly over time. The subjects can be individuals or pre existing groups of interest, such as species and regions, and the time refers to the metameter for the occasions of observation. Longitudinal data allow one to separate cohort and time effects. This article summarizes the special features of longitudinal studies, and overviews statistical study design and data analysis in longitudinal settings.
Mixed Outcomes
Authors: Molenberghs Geert , Geys Helena 
Published: September 2014

Abstract
Measurements of both continuous and discrete outcomes are encountered in many statistical problems. A common example is a developmental toxicity study, which typically involves exposing timepregnant animals (rats, mice) to an environmental agent during major organogenesis. The dams are sacrificed prior to normal delivery, at which time the uterus is removed and thoroughly examined. Among viable fetuses, the incidence of any malformation (binary data) and reductions in fetal weight (continuous data) are typically of primary concern, since both have been found to be sensitive indicators of a toxic effect. This motivates the formulation of a joint distribution with mixed continuous and discrete outcomes.
Aquatic Toxicology
Authors: Bailer A. John , Oris James T. 
Published: September 2014

Abstract
Water is the key to life, and aquatic toxicology evaluates the potential adverse outcomes associated with exposure to contaminated waters. These adverse outcomes can be observed at all trophic levels and can be framed at differing levels of ecosystem function. Statistical analysis of concentration‐response relationships provides one foundation for evaluating the impact of toxicants on aquatic systems and for setting exposure limits to such toxicants. This entry describes stressors on aquatic systems, considers ecosystem impact of stressors, reviews statistical methods that have been used for evaluating these impacts and considers current and future challenges for evaluating aquatic toxicants.
Capture‐Recapture Methodology
Authors: Gould William R. , Kendall William L. 
Published: September 2014

AbstractCapture‐recapture methods were initially developed to estimate human population abundance, but since that time have seen widespread use for fish and wildlife populations to estimate and model various parameters of population, metapopulation, and disease dynamics. Repeated sampling of marked animals provides information for estimating abundance and tracking the fate of individuals in the face of imperfect detection. Mark types have evolved from clipping or tagging to use of noninvasive methods such as photography of natural markings and DNA collection from feces. Survival estimation has been emphasized more recently as have transition probabilities between life history states and/or geographical locations, even where some states are unobservable or uncertain. Sophisticated software has been developed to handle highly parameterized models, including environmental and individual covariates, to conduct model selection, and to employ various estimation approaches such as maximum likelihood and Bayesian approaches. With these user‐friendly tools, complex statistical models for studying population dynamics have been made available to ecologists. The future will include a continuing trend toward integrating data types, both for tagged and untagged individuals, to produce more precise and robust population models.
Change, Detecting
Authors: Esterby Sylvia R. 
Published: September 2014

AbstractFundamental to the assessment of the effect of human activities on the environment is the detection of changes in environmental systems. It may be of interest to detect changes occurring over time, over space, or over both time and space. Although closely linked to the determination of status and the estimation of the size and components of change, detection is the primary consideration here. Methods for detecting change include: (i) graphical methods, exploratory, and more formal; (ii) tests of hypotheses, where the hypothesis characterizes the original status; and (iii) diagnostic tests that detect departures from a specified model. Most methods considered in this article are for changes over a single dimension and will often have been developed for data collected over time. However, such methods will usually be applicable to data collected in one dimension over space. Datasets for which more than one dimension needs to be taken into account generally require more modeling and thus are less often investigated from the detection point of view.
Detection Limits
Authors: Walshaw David 
Published: September 2014

Abstract

In analytical chemistry, the
detection limit
is the lowest level of an analyte that can reliably be detected by a well‐defined operating procedure. It is often confused with the
decision limit
, which is a threshold of measurement used to determine whether or not the analyte is present in a particular study. We discuss the history and confusion relating to the definition in a univariate context. We consider the detection limit as a function of the sources of variation within an operating procedure. Estimation of the detection limit assuming constant variance in one‐level designs and calibration designs is described, as well as a brief treatment of the assumption of non‐constant variance. We outline modifications for multiple future detection decisions. Finally we consider approaches to multivariate detection, when decisions are based on more than one quantitative signal.
Echelon Analysis
Authors: Patil G. P. 
Published: September 2014

Abstract

Quantitative spatial data are important inputs for fueling the engines of many environmental models that determine future implications of current resource use, policies, and interventions. End products of applying such models are often mappings of indexes for level of potential environmental impact, which then become guides to allocation of economic and technical resources for amelioration. Errors in quantitative spatial data will propagate through environmental models and find expression in the resulting impact indexes. However, the consequences of such errors for decision making may well depend upon where the errors occur. There may be relatively little confusion introduced by moderate errors occurring in a vicinity that otherwise has consistently high values of a variable. In contrast, errors compound confusion in areas that are highly variable. Errors can also substantially distort the apparent state of areas that otherwise have consistently low values of a variable. It is therefore desirable to have a systematic means of determining spatial organization in mappings of quantitative variables, both for input variables to environmental models and for indexes of potential impact generated by the models. Modern computer capabilities for visualization of surfaces are helpful in this regard, but their interpretation is subjective.
Echelons
are a method for objectively determining quantitative spatial structure for direct mapping, either with or without computer‐assisted visualization. Thus, they can facilitate analysis of errors associated with environmental models that take quantitative layers as input, or produce quantitative output layers, or both.
Ecological Study Design
Authors: Sheppard Lianne 
Published: September 2014

AbstractThe purposes of ecological studies range from descriptive (e.g. hypothesis‐generating) to inferential. The target of inference may be the group (calledecological inference) or the individual (calledbiological inference). Ecological studies are most appropriate for assessing the effects of interventions on populations, although the inference can be limited relative to an individual‐level study. Their applicability to etiological hypotheses about individuals is much more controversial, due to the risk of committing theecological fallacy. This fallacy is present when characteristics of individuals are wrongly inferred from grouped data. Although these reservations have a sound basis, most ecological study literature does not distinguish between issues inherent to the analysis of opportunistic observational data, and those pertinent to valid biological inference from group‐level analyses.
E
nvironmental
I
ndices
Authors: Dobbie Melissa J. , Dail David 
Published: September 2014

Abstract
An environmental index is the combination of multiple sources of information (i.e., indicators of ecological responses) about an environmental system (e.g., a stream) from potentially varying attributes of that system (e.g., instream processes, ecological processes, species richness, riparian vegetation, etc.). Environmental indices are typically developed to provide an overall snapshot of some feature of the environmental system but they are also used to compare similar environmental systems. The construction of an environmental index requires careful consideration of several important aspects of the individual environmental indicators that contribute to it, namely,

defining a theoretical framework for supporting indicator selection;

data preparation;

standardization of the potentially disparate indicators so that they can easily be combined into an index;

weighting and aggregation of the individual indicators; and

robustness and sensitivity of the decisions made in constructing an environmental index.

By considering all five steps, there is a better chance of producing a transparent, objective, and defensible summary of the attribute of the environmental system.
Escape Trajectory
Authors: Domenici Paolo 
Published: September 2014

Abstract
Escape responses consist of sudden accelerations as reactions to threatening stimuli and are present in most animals as a means of avoiding predation. Escape responses are currently receiving increased attention as valuable models for animal behavior, ecology, and neurobiology. The path along which an animal moves during an escape response (its escape trajectory) has attracted the interest of ecologists and behaviorists in relation to its functional role in predator–prey interactions. Escape trajectories (ETs) as considered in this article refer to the initial prey response to a predator's attack. Such a trajectory should be measured at the end of the main rotational motion present during the escape response, which usually corresponds to a specific kinematic stage of the animal's locomotion. Beyond this stage, prey may continue escaping along a zigzag path, especially if predators follow up their attack with a chase.
Forest‐Fire Models
Authors: Preisler Haiganoush K. , Ager Alan A. 
Published: September 2014

AbstractFor applied mathematicians forest‐fire models refer mainly to a nonlinear dynamic system often used to simulate spread of fire. For forest managers forest‐fire models may pertain to any of the three phases of fire management: prefire planning (fire risk models), fire suppression (fire behavior models), and postfire evaluation (fire effects models). In this context forest‐fire models may be statistical, rule‐based, process‐based, or a combination of all three.
Hydrological Extremes
Authors: Katz Richard W. 
Published: September 2014

Abstract
An accurate estimation of the probability of occurrence of an extreme hydrological event is often required for the effective development and management of water resources. Examples include characterizing extreme rainfall event magnitudes to predict urban runoff, determining river flow magnitudes to design a culvert or bridge opening at a road crossing, and determining the likely severity of a drought event during the design life of a water supply system. In each of these examples, an understanding of the hydrological response of a watershed is essential in order to obtain a reliable estimate of the relationship between extreme hydrological event magnitudes and the probability of these events occurring. The role of the statistical theory of extreme values is emphasized.
Impact Assessment: Assessing a Local Biological Effect With Before and After Data
Authors: Stewart‐oaten Allan 
Published: September 2014

AbstractThe design and analysis of monitoring studies to assess chronic biological effects of anthropogenic environmental change when data can be gathered before the change are discussed. Intervention Analysis compares Before and After time series at an Impact site. A special case, sometimes useful when the anticipated effects are “local,” is Before‐After, Control‐Impact (BACI), using comparison sites as covariates to reduce extraneous variance and serial correlation. Both sampling error and natural temporal processes cause uncertainty in inferences: both must be modeled. The models are themselves uncertain. The Impact site is not randomly selected, so these are observational studies, not experiments. Whether and how the change caused the “effects” must be based on their timing and on studies of mechanisms.