bluuebunny/crossref_metadata_2025 huggingface dataset:
Generalized
P
Values: Introduction
Authors: Ananda Malwane M.A. , Weerahandi Samaradasa
Published: September 2014

In many statistical problems involving nuisance parameters, conventional statistical methods do not provide exact solutions. For example, when comparing treatments in a one‐way or two‐way analysis of variance (ANOVA) model without the assumption of equal error variances, classical statistical methods do not provide exact solutions. As a result, practitioners often resort to approximate methods or asymptotic methods, which may perform poorly, specifically with small sample sizes. These kinds of situations arise in many environmental statistical applications. In particular, when dealing with small samples, as a result of using weak tests, experimenters often fail to detect significant differences between treatments. The generalized
P
value approach is a recently developed method based on exact probability statements rather than on asymptotic approximations. The generalized
P
value approach is an extension of the classical
P
value approach.
Goodness of Fit (Testing)
Authors: Deheuvels Paul
Published: September 2014

We present a selection of some common goodness‐of‐fit tests, with emphasis on the cases for which there exist analytical methods to evaluate their critical levels and asymptotic efficiency.
Model Evaluation and Performance
Authors: Beck B.
Published: September 2014

Whether the behavior of a model matches the behavior of the (real) system–sufficiently well–has always been a matter of great interest. In lay terms, these are the essential questions one would like to have answered in seeking to evaluate a model: (i) has the model been constructed of approved materials, i.e. approved constituent hypotheses (in scientific terms)? (ii) does its behavior approximate well that observed in respect of the real thing? (iii) does it work, i.e. does it fulfill its designated task, or serve its intended purpose?
Outliers
Authors: Aelst Stefan
Published: September 2014

Datasets frequently contain outliers which are observations that strongly deviate from the pattern in the majority of the data. Detecting outliers and measuring their influence on an estimation procedure is not trivial. In one or two dimensions graphical inspection of the data may be helpful, but in higher dimensions more reliable methods are needed. We discuss robust estimation to obtain estimates that can resist the influence of outliers and related outlier diagnostics to identify the outliers in a dataset.
Spatial and space‐‐time threshold exceedances
Authors: Angulo José M. , Madrid Ana E.
Published: September 2014

Threshold exceedance sets, defined by the coordinates where a certain critical level is exceeded, are of interest in many application fields in relation to risk analysis including deophysics, environmental sciences, engineering, astrophysics, finance, and insurance, among many others. In particular, many risk indicators can be formulated based on certain structural properties of threshold exceedance sets.

This article is structured in two parts. The first part reviews the two main approaches to describe the stochastic behavior of temporal threshold excesses, namely, the generalized Pareto distribution and nonhomogeneous Poisson processes, and collects references to some recent developments in the spatial and spatiotemporal contexts. The second part refers to some aspects of interest in relation to the structure of spatial threshold exceedance sets: on the one hand, geometrical measures such as the hypervolume and the Euler characteristic, which are related with certain probabilities of extremal occurrences; on the other hand, two different types of point processes, respectively based on centroids of connected components and on boundaryA‐exit points, which can be used depending on the specific structural properties of interest under study. The article concludes with a discussion and an illustration in the spatiotemporal context.
Subset Selection
Authors: Der Laan Paul Van
Published: September 2014

Assumek(k ≥ 2) populations are given. The associated independent random variables have continuous distribution functions differing only in their unknown location parameter. The statistical selection goal of subset selection is to select from the given set ofkpopulations a non‐empty subset of these populations so that the selected subset includes with confidence levelP*, withk−1 < P* < 1, the best population. In this context, best population means the population with the largest value of the location parameter. The subset selection approach is described in some detail and some properties are presented.
Censored Data Analysis
Authors: Portnoy Stephen
Published: September 2014

Censoring is a special form of missing data that arises in a wide variety of situations. Censoring occurs whenever the measurement is restricted to some specific range, but the values being measured can lie outside the range. Proper statistical analysis of such data requires specific adjustments, some of which are described here. Serious errors in inference can be made if censoring is not taken into account properly.
Depth
Authors: Serfling Robert
Published: September 2014

Depth functions represent a recently emerging powerful methodology in nonparametric multivariate inference. They provide multivariate notions of order statistics and generate quantile contours, outlyingness functions, and sign and rank functions. There are wide possibilities for constructing such functions, but these narrow considerably when criteria such as affine equivariance or invariance, computational ease, asymptotic behavior, and robustness are applied. We review basic definitions, leading examples, key properties, and selected applications.
Factor Analysis, Dynamic
Authors: Lippi Marco
Published: September 2014

We shortly reviewed small‐ and medium‐sized standard and dynamic factor analysis, and then concentrate on recent studies on high‐dimensional dynamic factor models. The latter allow the construction of consistent estimators of the factor model, which are based on principal components either in the time or in the frequency domain. Though mainly developed within Time Series Econometrics, high‐dimensional factor models are quite appealing for applications to environmental datasets.
Regression Trees
Authors: Leblanc Michael
Published: September 2014

Regression trees are a flexible statistical method for modeling the conditional distribution of a response variable given a vector of predictors or covariates. Trees have several advantages compared with classical parametric regression methods: they are completely nonparametric; they include powerful variable subset selection; they are robust to outliers in the covariate space and are used easily on a wide variety of data structures.
Runs Test: Theory
Authors: Ley Christophe , Paindaveine Davy
Published: September 2014

Given a sequence of two or more types of symbols, a run is defined as a succession of – one or more – identical symbols that are followed and preceded by a different symbol (or by no symbol at all). Runs tests are based on the length of the longest run or on the total number of runs. They figure among the oldest nonparametric procedures, and can be used in various setups. We review the different definitions of runs, their development over the last 100 years, describe the domains of application of runs tests, and discuss their extension to the multivariate case.
Measurement Error
Authors: Stefanski L.A.
Published: September 2014

Recognition that measurement error in the independent (predictor) variable of data used for regression modeling necessitates special methods of model fitting dates back to the late 1800s. In the ensuing 100 years attention was devoted almost entirely to measurement error in linear regression models, at which time advances in computing opened the door to the nonlinear statistical modeling generally, and to the study of measurement error in nonlinear regression modeling in particular. This article contains an introduction to the problem of measurement error in regression using linear regression measurement error models to illustrate key ideas. It includes a discussion of the types of measurement error commonly studied (classical and Berkson, differential and nondifferential) and how those types arise in practice. A summary of the consequences of ignoring measurement error when analyzing data measured with error is presented, with detailed results given for simple and multiple linear measurement error models. Pointers to the literature on both linear and nonlinear measurement error models are provided.
Mixed Effects
Authors: Drum Melinda
Published: September 2014

Mixed effects refers to the combination of both fixed and random effects in various types of linear and nonlinear regression models.
Nonparametric Regression Model
Authors: Opsomer Jean D.
Published: September 2014

Nonparametric regression is a rapidly growing and exciting branch of statistics, both because of recent theoretical developments and because of more widespread use of fast and inexpensive computers. Many methods are currently available, including kernel‐based methods, regression splines, smoothing splines and wavelet and Fourier series expansions. This article introduces the main types of smoothing methods and discusses the usefulness of nonparametric regression for analyzing datasets, particularly of the types encountered in environmental statistics.
Piecewise Linear Model
Authors: Küchenhoff Helmut
Published: September 2014

In many practical regression‐type problems we cannot fit one uniform regression function to the data, since the functional relationship between the response
Y
and the regressor
X
changes at certain points of the domain of
X
. These points are usually called break points or change points. In such situations, we apply regression models that are obtained by a piecewise definition of the regression function on intervals of the domain of
X
.
Random Effects
Authors: Piegorsch Walter W.
Published: September 2014

A brief introduction is given on incorporation of random‐effect components for the factors in an analysis of variance calculation. Extensions to mixed‐effects models and generalized linear mixed models are also noted.
Residual, Studentized
Authors: Paul Sudhir R.
Published: September 2014

A brief review of the procedures for detecting outliers in linear regression models using studentized residuals is provided. Several single and multiple outlier detection procedures and their advantages and disadvantages are discussed.
Robust Quantile Regression
Authors: JureČková Jana
Published: September 2014

Robust quantile regression is a collection of statistical procedures based on
regression, autoregression and conditional quantiles
and on
regression and autoregression rank scores
. These procedures can replace the classical estimation and testing procedures in the linear regression and autoregression models, based on least squares, in situations when we cannot guarantee that our observations follow the normal distribution. The character of these methods is nonparametric (i.e. they do not assume any specific probability distribution of the observations), and robust (with respect to outliers in the dependent variables). Moreover, the procedures based on the regression rank scores do not depend on an eventual nuisance regression, because the regression rank scores are invariant to the regression parameters, similarly as the ordinary ranks are invariant to the shift in location. Regression quantiles and regression rank scores are dual in the linear programming sense, but their relation also extends the duality of the quantiles (order statistics) and ranks from the location to the linear regression (autoregression) models.
Robust Regression
Authors: Jorgensen Murray
Published: September 2014

Unusual observations can have a large effect on least squares fits of regression coefficients. The most common defenses against this problem are residual plots and the diagnostic statistics based on residuals and leverages. These tools are most effective when the dataset is moderate in size and the analyst is in close touch with, or is, the person who collected the data. However, regression diagnostics become very time consuming to use in large data sets. In this situation it may be preferable to use moreresistantestimators that are relatively unaffected by even large changes to a small proportion of the data. In general, resistance must be ‘purchased’ at the cost of some loss of efficiency when the standard model assumptions hold, but these assumptions are seldom tenable in large data sets anyway. Regression diagnostics and robust regression are regarded by some as conflicting solutions to the problem of the sensitivity of standard least squares regression parameter estimates to unusual, possibly erroneous, observations. However, the two approaches can be seen as complementary. Residuals from robust fits can highlight interesting groups of observations not obvious from standard residual plots; leverage diagnostics can supplement the fits of some convenient, but only partially robust, regression methods.
Smoothing
Authors: Kafadar Karen
Published: September 2014

This article describes smoothing as an exploratory tool in data analysis, including an underlying model that motivates the need for smoothing, the goals and characteristics of good smoothers, general classes of smoothers (linear and nonlinear), and illustrations of the value of smoothing. Smoothing is related to the concepts of general fitting, parametric regression, and nonparametric regression, and the smoothers are illustrated as both one‐ and two‐dimensional data sets. Some insight is offered on choosing smoothing parameters, and some resources for computing smoothers and further references are also provided.
Splines in Nonparametric Regression
Authors: Zhang Hao Helen
Published: September 2014

This article is interested in splines as tools for visualizing and analyzing noisy observational data, and so restricts itself to smoothing splines and regression splines. The article first describes the univariate polynomial smoothing spline, which may be thought of as the forerunner of spline functions used in data analysis. It then describes cross‐validation and generalized cross‐validation (GCV) for choosing the smoothing parameter. After briefly describing regression splines, this entry then describes a number of generalizations of the univariate smoothing spline to various domains, which are obtained via the solution of a variational problem. These include the thin plate spline, the histospline, splines on the sphere, vector splines on the sphere, hybrid splines, partial splines, and smoothing spline analysis of variance (ANOVA) models on complex domains. Computational issues are also discussed. The last two sections focus on the topics of variable selection and model selection in nonparametric regression. Various penalized regression with penalty for function shrinkage and selection are described. Publicly available software is mentioned along the way.
Distance Sampling
Authors: Thomas Len , Buckland Stephen T. , Burnham Kenneth P. , Anderson David R. , Laake Jeffrey L. , Borchers David L. , Strindberg Samantha
Published: September 2014

Distance sampling is a widely used group of closely related methods for estimating the density and/or abundance of biological populations. The main methods areline‐transect samplingandpoint‐transect sampling. In both cases, observer(s) perform a standardized survey along a series of randomly located lines or points, searching for objects of interest (usually animals or clusters of animals). For each object detected, they record the distance from the line or point to the object. Not all objects will be detected, but a fundamental assumption of the basic methods is that all objects that are actually on the line or point are detected. The key to distance sampling analyses is to use the observed distances to fit adetection functionthat describes how detectability decreases with increasing distance from the transect. The fitted function is used to estimate the average probability of detecting an object; from here, one can readily obtain point and interval estimates for the density and abundance of objects in the survey area. Various extensions to the basic methods allow assumptions to be relaxed, and include methods that integrate capture‐recapture and distance sampling, as well as methods to model spatial variation in density.
Edge Effect
Authors: Stevens Jr Don L.
Published: September 2014

A common objective in sampling a population of biological, ecological, or environmental concern is an inference about the mean or total of some attribute per unit area. In many instances, population attributes are measured in the field by compositing information collected on plots of non‐negligible extent. For example, a forest inventory may count the number of trees and measure their basal area on circular plots. Fish population size might be estimated by counting the number of fish in selected stream reaches in a basin. Estimates of total biomass in a region could be obtained by collecting all biomass in randomly located quadrats. Unfortunately, it is well documented that this general method can result in a discrepancy, often callededge‐effect bias, between the expected value of the estimator and the true value of the total. This discrepancy results from lack of coherence between the measured density and the true density stemming from injudicious handling of plots that are near to or actually straddle the domain boundary. Unless such plots are treated appropriately, population elements near the boundary are weighted incorrectly in computing the overall total. Because edges often have properties that are distinctly different from interior properties, giving an incorrect weight to the edges can result in substantial bias. This article gives an explicit definition of edge‐effect bias, provides a sufficient condition for its absence, and discusses techniques to counter it.
Generalized Regression Estimators
Authors: Park Mingue , Fuller Wayne A.
Published: September 2014

Regression and regression related procedures have been used often in survey estimation. We review the basic properties of regression estimators and investigate variance estimation for regression estimators. Some range restricted regression procedures are also explored.
Sampling, Environmental
Authors: Stehman Stephen V. , Overton W. Scott
Published: September 2014

Environmental sampling encompasses methods for collecting and analyzing data to address questions in diverse fields such as ecology, environmental engineering, environmental policy, fisheries, forestry, and wildlife science. The goal of sampling is to estimate collective properties or parameters (e.g., means, totals, and proportions) characterizing the attributes or measurements defined on a universe. A universe may consist of discrete objects such as lakes, wetlands, stream segments, forest stands, plant or animal populations, environmental organizations, or recreational users, or a universe may be defined spatially, for example by delineating an area on a map. Environmental sampling objectives typically address questions of status and/or trend. Status refers to current attributes of the universe. For example, how many acidic lakes are there in the northeast United States potentially sensitive to acidification, or how many individuals of a rare plant population exist? Trend focuses on how attributes of a universe are changing over time. Are certain amphibian populations declining in South American forests? Is tree disease increasing following a damaging ice storm? How much agricultural land is being converted to residential and commercial use? These collective properties of status and trend are estimated from a sample, which is a subset of the universe. A sampling strategy consists of a sampling design, which is the protocol for selecting the subset of the sample, and the estimator (formula) of a parameter. Good environmental sampling practice requires a judicious choice of both sampling design and estimator to create a sampling strategy that is practical and yields precise estimates of parameters.
Benchmark Analysis
Authors: Crump Kenneth
Published: September 2014

The benchmark approach is a statistical procedure applied to dose–response data from toxicologic, clinical or epidemiologic studies. It can be used to set health standards or guidelines for exposures to toxic substances. Such guidelines include those promulgated by the United States Environmental Protection Agency (USEPA) called references doses (RfDs), for oral exposures, or reference concentrations (RfCs), for exposures through the ambient air. The USEPA defined the RfD as ‘an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population, including sensitive subgroups, that is likely to be without an appreciable risk of deleterious effects during a lifetime’.
Biomarkers
Authors: Thurston Sally W.
Published: September 2014

Biomarkers are cellular, biochemical, or molecular changes in a biological system. Biomarkers are often broadly classified as markers of exposure (either of internal dose or of the biologically effective dose), markers of preclinical biological effects, markers of susceptibility, or markers of disease. Biomarkers of exposure that are known to be on the causal pathway to cancer have been used to help identify environmental carcinogens. Biomarkers of disease are often associated with a premalignant condition, but recently epigenetic changes such as DNA methylation have been proposed as disease biomarkers. Markers of susceptibility include genetic polymorphisms that affect the probability of proceeding from one disease state to the next. Challenges in using biomarkers in cancer research include the inability to measure biomarker levels in the target tissue of controls, and the possibility that the disease process may alter biomarker levels. Established and well‐validated biomarkers, as well as new biomarkers, hold promise for a better understanding of the cancer process and for identifying individuals at increased cancer risk.
Diet, Measurement Of
Authors: Willett Walter C.
Published: September 2014

Comparisons of disease rates among countries and animal studies have strongly suggested that aspects of diet are major determinants of cardiovascular diseases and various cancers. However, the potential for confounding by other lifestyle characteristics, and the desire to identify the critical components of diet, created the need for more detailed studies of diet and disease within populations. In addition to interest in dietary factors as primary exposures, diet may also be important as a modifier or confounder of associations between other environmental factors and disease. For example, constituents of certain vegetables are potent inducers of enzymes involved in the detoxification of carcinogens, and persons with a specific occupational exposure could well have different diets than those not exposed. Thus, the measurement of dietary factors may be desirable even when they are not of direct interest.
Disease Mapping, Hierarchical Models For
Authors: Waller Lance A.
Published: September 2014

Hierarchical models for disease mapping.

Hierarchical models provide a convenient framework to combine information across multiple observational units. Such models are particularly convenient for small area estimation of disease rates to improve statistical precision of estimates associated with small local sample sizes. We review the basic framework of model‐based approaches for combining information across small administrative units through the use of random effects. Varying definitions of the random effects distribution allows one to combine information from all locations, neighboring locations, or a combination of both.
D
ose‐
R
esponse
M
odeling for
C
lustered
D
ata
Authors: Chen James J.
Published: September 2014

Clustered data refer to a set of correlated responses. For example, different animals represent different genetic values inherited from parents that may also be nested within herds or spatial entities. Clustered data also result from study designs, such as repeated observations on the same subject or sampling from correlated subsamples. A great deal of statistical research on dose response modeling of clustered data is devoted to animal reproductive and developmental toxicity studies. In developmental toxicity experiments, chemicals are administered to the dam (female animal), but the observations are made on individual fetuses. The clustering of fetuses within the litters induces the cluster (litter) effect. Modeling the clustered data should account for the correlation among the individual responses within a cluster to draw valid and efficient inferences. Two common approaches for fitting dose response models to clustered data are the likelihood‐based and generalized estimating equations (GEE) (or quasi‐likelihood) approaches.
Fertility Studies
Authors: Dunson David B.
Published: September 2014

In recent years there has been increasing concern that human exposure to environmental agents may disrupt the endocrine system and alter reproduction. For example, some studies have observed secular declines in semen quality over the past 50 years, and several widely used chemicals have been shown to adversely effect human fertility. This article briefly reviews statistical methods for assessing environmental effects on human fertility, with particular emphasis on methods for analysis of data from time to pregnancy (TTP) studies.
Transmission/Disequilibrium Test
Authors: Seillier‐moiseiwitsch Françoise , Niu Tianhua
Published: September 2014

The transmission/disequilibrium test (TDT) can be utilized as a test of linkage between a marker and a disease locus or as a test of association between these loci. It relies on the marker information from triads consisting of parents and their affected child. Its properties depend on the purpose it is used for. Its validity for stratified populations makes it particularly attractive. Also, it is often more powerful than allele‐sharing methods for complex diseases.
BACI
Design
Authors: Smith Eric P.
Published: September 2014

The purpose of impact assessment is to evaluate whether a stressor has changed the environment, which components are adversely affected, and to estimate the magnitude of the effects. The evaluation of impact involves comparative methods. Early approaches to impact assessment involved the use of computer simulation models to predict the impact. Decisions were then made based on the soundness of those predictions. Although the data used to interpret effects are quite varied, the methods for analysis are often quite similar and involve comparison of impact areas with control areas. When information is available prior to the potential impact, the design is often referred to as a
Before–After Control‐impact
(BACI) design. Several variations on the basic design have been proposed and are discussed in this article.
Capture–Recapture Models, Spatially Explicit
Authors: Gopalaswamy Arjun M.
Published: September 2014

Animal density is a parameter of basic interest to field biologists. But owing to logistical and analytical constraints, estimating animal density has been very challenging. Spatially explicit capture–recapture (SECR) models were developed to address this particular, long‐standing problem. In this article, some of the important “milestones” in the SECR literature are discussed. The basic idea in SECR models is to include information about locations of animal captures into the modeling so that density is estimated directly. The early SECR type of approach involved estimation of density via simulations and inverse prediction. The first direct SECR estimator was likelihood‐based, which was more flexible and allowed for inclusion of covariates. In parallel began the development of Bayesian versions of the SECR models. These allowed for greater flexibility and for extensions which could, in addition, also estimate population vital rates. SECR models have witnessed several applications, ranging from analysis of camera‐trap data on large cats to acoustic signal data from birds. The future of SECR models is expected to go far beyond density estimation. It has great potential to answer some long‐standing questions about how behavioral processes drive population dynamics in wild animals.
Capture–Recapture Sampling Designs
Authors: Schwarz Carl
Published: September 2014

Good study design is necessary to obtain unbiased and efficient results from capture–recapture (CR) studies. We describe the considerations necessary for successful completion of CR studies. Rather than focusing on analytical issues, we discuss the stochastic processes that give rise to the data derived from such studies. We focus on the attributes of studies necessary to ensure that the assumptions of analyses associated with CR studies are met. We hope to expose investigators to data collection issues associated with classic CR designs, and also to point out novel applications of these designs.
Co‐limitation
Authors: Wacker Alexander
Published: September 2014

Understanding the interactions in resource limitations is a crucial challenge for ecological sciences. If organisms are simultaneously limited by more than one nutrient, they face a multiple resource limitation, which is often described by the term
co‐limitation
. Though the concept of co‐limitation has been successfully applied to describe nutrient‐limited growth responses of plants, this concept has been poorly applied to the nutrient‐limited growth of animals. This article provides essential insights into the concept of co‐limitation and its different facets, in particular the differences among modeling equations.
Decision Support Systems, Environmental
Authors: Nathanail C. Paul
Published: September 2014

Decisions involving environmental aspects are complex. The underlying environmental processes are tackled by teams that are invariably multidisciplinary in nature and involve sundry specialists: earth, physical, chemical, geochemical, biological, benthic, and ecological sciences. Many decisions, such as water resources allocation, irrigation and agricultural practices, toxic waste management, industrial effluent regulation, acid rain mitigation, and climate change accommodation are interrelated. The solution or mitigation of one problem usually affects another. In addition, the constraints to these solutions are legion: economic considerations for remedial costs, social values of clean water and air, political and public opinions on biodiversity, and so on. Environmental decision makers and policy advisors need to consider all these complex factors in their decision‐making and policy‐building processes. The purpose of this article is to discuss the tools that an environmental manager may need to support decision‐making processes. With the advent of information technology, this article focuses on the possible use of computer systems to provide the support. Their design, implementation, advantages, and disadvantages are discussed and examples of applications and future directions are given. These systems are calledenvironmental decision support systems.
Diversity Measures
Authors: Solow Andrew R.
Published: September 2014

One way in which the term diversity is used in ecology is to characterize the evenness of the relative abundances of species (or other types) in a community (or other collection of organisms). A variety of measures that have been proposed for this purpose are reviewed.
Environmental Forensics
Authors: Johnson Glenn W.
Published: September 2014

Environmental forensics is the scientific evaluation of environmental data for the purposes of evaluation of the history and sources of environmental contamination. Forensics investigations are often conducted in legal rather than purely academic context. Scientific methods that may be used in such studies are large and diverse: limited only by relevance to the problem at hand, and the degree to which the method has proved reliable and has been accepted within the scientific community. Several common data analysis methods that have seen extensive use in forensic studies are principal components analysis, receptor models, and simple graphical displays such as concentration maps.
E
xposure
A
ssessment
Authors: Brown Kathleen Ward , Levy Jonathan
Published: September 2014

Human exposure to an agent has been defined as an event consisting of contact with a specific concentration of the agent at a boundary between the human and the environment (e.g., lung or skin) for a specific amount of time. Exposure therefore refers to the intensity, frequency, and duration of contact with an agent, which is distinct from the actual amount of the agent absorbed or received by target organs (the dose). This generally entails identification of population(s) exposed as well as the sources of the agent, pathways for the agent to contact the target, exposure routes, and the uncertainties in the assessment. The goal of exposure assessment is often to determine the distribution of exposures to a specific agent across individuals, which provides both the population mean exposure (relevant for quantifying population risks) and the exposure levels for more highly exposed or susceptible individuals.
Extremal Events
Authors: Katz Richard W.
Published: September 2014

The term extremal event is commonly used in relation to environmental occurrences that impact greatly on day‐to‐day life, for example, storms and floods. Although it is clear that such events are extremal events, it is not clear, more generally, what an extremal event is, or what an extremal event is not for that matter. We discuss the role of extremal events in extreme value theory and in several application areas so that we may identify any common underlying themes.
Fish Population Estimation
Authors: Routledge Rick
Published: September 2014

Fisheries management decisions typically depend heavily on suitable estimates of abundance, or at least indices that can be used to assess trends. This article provides an overview of the more commonly used approaches with commentary on their relative advantages and weaknesses.
Impact, Environmental
Authors: Smith Eric P.
Published: September 2014

Environmental impact refers to the potential or realized effect of a planned or existing project or anthropogenic displacement on the environment. Impact assessment is the analysis of the impact and is an integral part of environmental impact assessment and statements, and the strategic environmental assessment that is an essential component of environmental decision making and policy.
Kriging, Asymptotic Theory
Authors: Du Juan
Published: September 2014

The fixed‐domain asymptoic theory regarding the impact of misspecified covariance structure on the optimality of spatial interpolation is reviewed. Conditions in terms of equivalence of measures, similar tail behavior of spectral densities, and the distance of variances after orthogonalization are provided to ensure the pseudo‐BLP to be asymptotically optimal and the assessment of the mean squared error to be comparably accurate.
Ranked Set Sampling
Authors: Patil G.P. , Surucu Baris , Egemen Didem
Published: September 2014

Environmental monitoring and assessment typically requires observational data as opposed to data obtained from controlled experiments. Obtaining such data requires identification of sample units to represent the population of concern, followed by selection of particular units to quantify the characteristic(s) of interest. Sample units are generally the smallest possible units of measurement, such as plots, soil cores, and individuals, while typical characteristics of interest include biomass, chemical concentrations, or “head counts.” Typically the most expensive part of this process is laboratory analysis, while identification of potential sample units is comparatively simple. One can therefore achieve great observational economy if one can identify a large number of sample units to represent the population of interest, yet only have to quantify a carefully selected subsample. This potential for observational economy was recognized for estimating mean pasture and forage yields in the early 1950s when McIntyre proposed a method, later coined ranked set sampling (RSS) by Halls and Dell, and currently under active investigation in various quarters.
Risk Assessment, Ecological
Authors: Fox David R. , Halbrook Richard S.
Published: September 2014

Ecological Risk Assessment is a process for identifying, estimating, assessing and communicating both the likelihood and consequences of adverse outcomes on the biotic environment. These adverse outcomes can be associated with both natural (eg. earthquakes, floods) and human (eg. chemical pollution, mining, logging) activities or events. The scale and complexity of ecological risk assessments can vary from recommendations based on simple descriptive studies to regulatory frameworks arising from the application of detailed scientific investigations and advanced statistical analyses. While the methods and procedures have evolved constantly over the last 80 years or so, there remain gaps in key understanding of ecosystem processes and parameterisation of models to represent them. This ‘model incertitude’ represents a significant source of uncertainty in the utility of ERA outputs and is the focus of more contemporary methods. This article briefly traces the origins or ERA, highlighting similarities and differences with other variants of risk assessment before discussing common paradigms and structured approaches to the conduct of an ERA.
Sample Support
Authors: Goovaerts Pierre
Published: September 2014

Sample support generally refers to the length, area, or volume associated with a measurement or observation. The term originates in the field of geostatistics, primarily from mining applications. To appreciate the issues associated with this term, this entry provides (i) a more complete definition of sample support as used in geostatistics; (ii) a description of the problems that arise in working with data of differing supports; (iii) a brief overview of the solutions to change of support problems.
Spacing Trials
Authors: Burkhart Harold E.
Published: September 2014

One of the most important factors that is within the direct control of managers of plant crops is plant spacing. Managers of agronomic, horticultural, and forestry cropping systems must decide on an initial planting density. Economic considerations dictate that one strives for the ‘optimal’ spacing of plants. The optimum spacing will, of course, vary depending on the management objective. Designed spacing experiments provide reliable information on optimal density for given product objectives.
Spatial Covariance
Authors: Sampson Paul D.
Published: September 2014

Modeling of the spatial dependence structure of environmental processes is fundamental to almost all statistical analyses of data that are sampled spatially. Such analyses address tasks such as spatial estimation (Kriging), monitoring network design (Optimal spatial design), as well as the basic scientific characterization of the second‐order properties of these processes. This article reviews the literature on nonstationary spatial covariance modeling primarily under the headings of smoothing and kernel methods, basis function models, process convolution models, and spatial deformation models. This discussion raises some general issues and comments on new and forthcoming developments.
Species Diversity
Authors: Latimer Andrew M.
Published: September 2014

Species diversity is the number of species present in an area or sample, together with the characteristics of their relative abundance distribution, especially its evenness. Species diversity is only one dimension of biodiversity, but because species are fundamental units of ecology and evolution, and because most diversity data consist of species presence/absence, species diversity is often central to assessing and conserving biodiversity. After noting major conceptual and practical issues with species diversity and species richness, we discuss how best to estimate them from samples both for estimating total species number and for comparing diversity across samples and areas. Despite errors associated with extrapolation, recent research has demonstrated that in many cases, given adequate sampling, both nonparametric and parametric methods can estimate total species richness and effectively standardize across samples. Much research has focused on developing and interpreting compound diversity measures that incorporate patterns of both richness and abundance, some with weightings for additional factors such as rarity, phylogenetic distinctness, and ecological function. For a well‐known set of these indices, including the Shannon and Simpson indices, lognormal mean and variance, log‐series α and related neutral theory parameters, we discuss their consistency in ordering samples and their sensitivity and dependence on sample size. Finally, we touch upon major global patterns of diversity including estimates of the number of species on earth (5 million to 50 million) and large‐scale diversity gradients.
Source Apportionment
Authors: Watson John G. , Chow Judith C.
Published: September 2014

Source apportionment models use variations in air pollutant composition, time, and space to infer source types and quantify their contributions. Marker species are sought in source emissions that minimize collinearity among the pollutant patterns found in many source types. Engine exhaust, biomass burning, geological material, sea salt, and secondary sulfates and nitrates can be separated and quantified with commonly measured elements, ions, and carbon fractions. More detailed organic compounds, isotopic abundances, and particle morphology are usually needed to distinguish source subtypes, such as diesel and gasoline engine exhaust. The data sets are noisy, and uncertainties in source contribution estimates must be assessed. At a minimum, variability in the chemical patterns in emissions and ambient concentrations should be quantified and propagated. Sample sizes should encompass a range of source contributions, which can be accomplished by locating sampling sites and selecting measurement periods where large contributions are expected. A “weight of evidence” approach assesses uncertainties by the application of several source apportionment techniques with reconciliation of discrepancies for the source contribution estimates.
Geographic Information Systems (GIS), Spatial Statistics In
Authors: Cressie Noel , Liu Desheng
Published: September 2014

A Geographic Information System (GIS) is a networked collection of hardware and software tools that recognizes georeferencing in the data and features the georeferencing in database management, data analysis, and visualization of results (principally maps) from the analysis. This combination of geography and computer science has been known as geographic information science. Spatial statistics in GIS has grown in importance, however, spatial databases in a GIS can be very large. It may be that, without some form of dimension reduction, only the most simple statistical methodologies will scale up.
State‐Space Methods
Authors: Koopman S.J.
Published: September 2014

The state space model provides an unifying framework for analyzing time series. For the classical Box–Jenkins analysis, state space methods are useful when estimating the autoregressive and moving average coefficients by exact maximum likelihood and when missing values are present. In the case of unobserved components time series models with interpretable components such as trend, seasonal, cycle and irregular, state space methods provide the tools for estimation, signal extraction and diagnostic checking. Indeed, all linear time series models can be cast into state space including dynamic regression models and cubic spline smoothing models.
Structure–Property–Processing Relationships for Stainless Steel Foams Made by Mechanical Aeration of Powder Slurries
Authors: Lin Xi , Kennedy Andrew R.
Published: September 2014

A simple process, based on the mechanical aeration of a powder slurry, followed by gel casting and sintering, has been used to produce 316L stainless steel metal foams. The foams have spherical pores, with a small size range, connected by windows, to create an interconnected, open cell structure. Despite the simplicity of the process, the foams have good and reproducible foam structures and compressive mechanical properties, although the ability to vary the pore size and density is limited. While foams of this type are more likely to be suited to applications where the connected nature of the porosity is exploited, they demonstrate mechanical properties on a par with foams and porous metals specifically suited to structural applications, where specific strength and energy absorption are paramount. Foams made in this way therefore demonstrate the multi‐functionality that is required of them if they are to be exploited and are thus exciting prospects for further development.
Photoresponsive Soft‐Robotic Platform: Biomimetic Fabrication and Remote Actuation
Authors: Jiang Weitao , Niu Dong , Liu Hongzhong , Wang Chaohui , Zhao Tingting , Yin Lei , Shi Yongsheng , Chen Bangdao , Ding Yucheng , Lu Bingheng
Published: September 2014

Biomimetic microsystems, which can be driven by various stimuli, are an emerging field in micro/nano‐technology and nano‐medicine. In this study, a soft and fast‐response robotic platform, constituted by PDMS/graphene‐nanoplatelets composited layer (PDMS/GNPs) and pristine PDMS layer, is presented. Due to the differences in coefficient of thermal expansion and Young's modulus of the two layers, the bilayer platform can be driven to bend to the PDMS/GNPs side by light irradiation. The robotic platform (1 mm in width and 7 mm in length) can be deflected about 1500 μm by near infrared irradiation (nIR)(808 nm in wavelength) within 3.4 s, and excellent reversibility and repeatability in actuation are also revealed by sweeping and multicycle light irradiation. The experiments also show that, the presented bilayer platform in various shapes, that is, fish‐like shapes, can float and swim to perspective location in fluid (i.e., water), whose moving directions and velocities can be remotely adjusted by light, indicating an excellent light‐actuation ability and well controllability. The results may be not only hopeful in developing light‐driven drug‐delivery platform, but also the bio‐robotic microgrippers applying in vivo and in vitro.
Electronic structure of In3Se4 and In3Te4 monolayers from ab‐initio calculations
Authors: Debbichi Lamjed , Eriksson Olle , Lebègue Sébastien
Published: September 2014

First principles calculations have been used to calculate the interlayer binding energy, the phonons frequencies, and the electronic structure of the In3X4 layered systems, with X = Se or Te. Using density functional theory corrected to take into account van der Waals interactions, the binding energy was found to be ∼ 41‐44 meV for In3Se4 and ∼ 53‐58 meV for In3Te4. Moreover, the absence of imaginary frequencies in the phonons bandstructures demonstrates the dynamical stability of the isolated layers. These facts suggest that isolated layers of In3Se4 and In3Te4 could be obtained by means of exfoliation from the bulk.