bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discriminant Analysis
Authors: Mclachlan Geoffrey J. 
Published: January 2013

AbstractIn this article, we consider discriminant analysis where classification is undertaken in the context where there are a number of classes and the observed data (the so‐called training data) are of known classification with respect to these classes. We consider both parametric and nonparametric approaches to the formation of allocation rules from the available training data. The coverage starts with Fisher's linear discriminant function, which was the initial attempt to perform discriminant analysis in its modern guise. The estimation of the error rates of the sample‐based rules so formed are considered. Particular attention is given to the classification of high‐dimensional data where the number of training observations is small relative to the large number of available variables.
EcosystemMonitoring
Authors: Mcclintock Brett 
Published: January 2013

AbstractThe world's population has increased 6 times over the past 200 years. It has doubled over the past 40 years. Air and water pollution, climate change, loss of species, soil erosion and salinization, desertification, loss of forest, and a range of other undesirable environmental alterations have accompanied this unprecedented increase in human numbers. While the rate of population growth is declining, human pressure on the environment will continue to increase as human standards of living and per capita consumption increase, and it is clear that a fundamental change must occur in the relationship between humans and the global ecosystem if we are to avoid highly undesirable environmental change. Development of a sustainable relationship between humans and the environment requires an understanding of, and respect for, both human needs and desires, and the structure and functioning of the world's ecosystems. Policies and resource management practices must be developed that sustain desired ecosystem conditions and functions. To ensure that these policies and practices are effective, the condition of the world's ecosystems must be monitored.
Sampling, Environmental
Authors: Stehman Stephen V. , Overton W. Scott 
Published: January 2013

AbstractEnvironmental 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.
E
uropean
E
nvironment
A
gency (
EEA
)
Authors: El‐shaarawi Abdel 
Published: January 2013

Abstract
The decision to establish European Environment Agency (EEA) was made by the European Union in 1990 and it began its work in earnest in 1994. Its objective is to provide for decision‐makers and the public sound and independent information on the environment. It has a total of 32 member countries and 7 cooperating countries.
Generalized Additive Models
Authors: Grego John M. 
Published: January 2013

Abstract
Generalized additive models replace the linear predictor in a linear model or generalized linear model framework with an additive predictor composed of nonparametric or semiparametric functions of the explanatory variables. Similarly to generalized linear models, the predictor is linear in the link function of the response mean, and the response variable can have a non normal distribution. The last decade has seen interesting new approaches to estimation of generalized additive models, and the development of a mixed effects framework for these models.
Generalized Estimating Equations
Authors: Darnell Ross 
Published: January 2013

AbstractInterdependence is a characteristic feature of many environmental processes. However, the complexities for statistical analysis introduced by such interdependence have hampered the progress of environmental science. Why this is so can be understood by considering the workhorse of standard statistical analysis, thegeneralized linear model(GLM). A typical GLM divides variation in some measure into a systematic part and a random part. Typically substantive theory guides the construction of the model for the systematic part. For the random part, although some guidance may be obtained from knowledge of the distributional consequences of a simple and plausiblestochastic processthat might be responsible for this variation, and from experience gained with other comparable data, we rarely have complete confidence in our choice of distribution. In the case of spatial, longitudinal and multivariate outcomes the choice is among multivariate distributions since we need to describe the association across outcomes as well as within outcome variability. This becomes especially difficult for problems where the multivariate normal distribution is not a suitable choice for the random part of the model, since the available alternative distributions lack flexibility. Under these circumstances it is often helpful to use generalized estimating equations (GEEs). The GEE approach, while making weaker distributional assumptions than that required for a fully parametric linear model, maintains the properties of consistency and asymptotic normality of parameter estimates.
Groundwater Monitoring and Assessment
Authors: Cai Zuansi , Thornton Steven F. , Lerner David N. 
Published: January 2013

Abstract
Groundwater is an important component of water supplies in most countries. It exists in aquifers under unsaturated and saturated conditions. The flow of groundwater is primarily controlled by the aquifer physical properties and hydraulic pressure gradients. The transport of solutes dissolved in groundwater is influenced by a combination of physical, chemical, and biological processes. Hydrodynamic dispersion is an important transport process, which influences the fate of contaminants in groundwater particularly the biodegradation of organic compounds. The quantitative analysis of groundwater resources can be completed using water balances and numerical modelling tools. Groundwater chemistry is controlled mainly by the aquifer composition but is vulnerable to contamination by many naturally occurring processes and human activities from point and diffuse sources. Modeling tools are generally used to simulate groundwater flow and solute transport in aquifers. Statistical analysis using Monte Carlo methods is often used to describe the uncertainty in parameters included in groundwater modeling studies. Groundwater quality assessment must be undertaken using monitoring well designs and sampling methods, which provide representative samples for analysis. This objective is important when using groundwater chemistry data to interpret the performance of in situ groundwater remediation concepts such as monitored natural attenuation.
Hansen‐Hurwitz Estimator
Authors: Christman Mary C. 
Published: January 2013

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.
Disease Mapping, Hierarchical Models For
Authors: Waller Lance A. 
Published: January 2013

AbstractHierarchical 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.
Hydrological Extremes
Authors: Katz Richard W. 
Published: January 2013

AbstractAn 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.
Conditional Entropy Profiles
Authors: Patil G.P. 
Published: January 2013

Abstract
When a landscape is represented by multiple landcover types, the challenge is to define a measurement of landscape fragmentation that can be applied to any defined geographic area. Such a measurement would ideally allow quantitative determination for when a landscape pattern has changed significantly, either within the same geographic extent over time, or among different locations within a similar ecoregion. Of interest is ecosystem identification, such as may be delineated by watershed boundaries close to a critical point of into a different, possibly degraded, ecosystem where the landscape matrix has become developed land. Such a measurement of landscape fragmentation can then be a primary component of an ecosystem risk assessment.
Line‐Transect Sampling, New Approaches
Authors: Melville Gavin , Cowling Ann 
Published: January 2013

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.
Markov Chain Monte Carlo (MCMC)
Authors: Barnes Katherine 
Published: January 2013

AbstractRealistic statistical models often give rise to probability distributions that are computationally difficult to use for inference. Fortunately, we now have a collection of algorithms, known as Markov chain Monte Carlo (MCMC), that has brought many of these models within our computational reach. MCMC is a simulation technique that allows one to make (approximate) draws from complex, high dimensional probability distributions. Over the last 20 years a staggering amount of research has been done on both the theoretical and applied aspects of MCMC. This article does not intend to be a complete overview of MCMC but only hopes to get the reader started in the right direction. To this end, this article begins with a general description of the types of problems that necessitate the use of MCMC. It then introduces the fundamental algorithms and addresses some general implementation issues. Discussion of the Markov chain theory underpinning MCMC algorithms has intentionally been avoided.
Modeling, Environmental
Authors: Solow Andrew R. 
Published: January 2013

AbstractIn discussing environmental models, it is useful to distinguish between process‐based or mechanistic models and phenomenological or empirical models. Although the distinction is not always clear, mechanistic models are based on a scientific theory about the underlying environmental processes, whereas empirical models are based on observation alone. This article focuses on statistical analysis under mechanistic environmental models. This is not to imply that empirical environmental models are less interesting or less useful. Rather, it is because empirical environmental models are, from a statistical perspective, no different from empirical models in other fields. While the methods of statistical analysis under mechanistic environmental models are certainly not unique, such models tend to share certain characteristics that determine in part the kind of statistical issues that arise, and even the kind of statistical methods used to address these issues.
M
ultistage
D
esign
Authors: Christman Mary C. 
Published: January 2013

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.
Nearest Neighbor Methods
Authors: Dixon Philip M. 
Published: January 2013

AbstractThis article discusses nearest neighbor (NN) methods for the analysis of spatial point patterns and the analysis of field experiments. The distance from a point to its NN, or quantities derived from that distance, can be used to test whether the observed locations are a realization of a specific spatial point process. Edge effects, caused by recording data only within a bounded study area, can be important. When locations are marked (e.g.,by the species of plant), NN methods provide information on the relationship between the marks. NN methods for field experiments can be used to model local spatial variability.
Network Ecology
Authors: Borrett Stuart R. , Christian Robert R. , Ulanowicz Robert E. 
Published: January 2013

Abstract
Network ecology is the investigation of complex ecological systems (e.g., metapopulations, communities, ecosystems, landscapes) by modeling them as networks of interactions and applying network analyses. It is a subdiscipline of both ecosystem ecology and network science. A network model is composed of set of nodes and one of edges that map a relationship between these nodes. For example in ecosystems, nodes might represent species, functional groups, or nonliving resource pools (e.g., detritus), and weighted, directed edges show the flow of energy‐matter from one node to another. Many algorithms exist for analyzing network organization, functioning, and in some cases dynamics. These algorithms can be found in a limited number of readily available software packages. This article is an introduction to network ecology through the lens of ecosystems. It traces the origins of ecological network analysis, reviews a family of algorithms and software to analyze such networks, and then delves into more detail about selected methods. We conclude by highlighting recent advances in ecological science due to the application of network ecology.
Nonparametric Regression Model
Authors: Opsomer Jean D. 
Published: January 2013

AbstractNonparametric 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.
S
patial
D
esign,
O
ptimal
Authors: Zimmerman Dale L. 
Published: January 2013

Abstract
Statistical inferences from spatial data are affected substantially by the spatial configuration of the sites where those data are observed. Thus, it is natural to try to select a configuration that optimizes, in some sense, the quality of the desired inference(s). This article lays out the classical framework for optimal model‐based spatial sampling design. Prediction error variance design criteria are featured, but optimality criteria for estimating model parameters are also mentioned. Optimal designs may be built “from scratch” or by expanding or contracting an existing design.
Pelagic Ecology
Authors: Reynolds Colin S. 
Published: January 2013

Abstract

The term ‘pelagic zone’ refers essentially to the open ocean, beyond the continental shelves. However, ‘the pelagic’ is understood by freshwater ecologists to mean simply ‘the open water’ of large lakes and reservoirs. In either instance, the notion of a liquid environment, remote from the shores and bottom of the containing basin, is clear. In extent, the marine pelagic is, by far, the greatest of the world's major ecosystems, covering some 361 × 10
6
km
2
(or 71% of the planet's surface) and accommodating an estimated volume of 1.35 × 10
9
km
3
. In contrast, the aggregate volume of the world's lakes and rivers is a relatively modest 225 × 10
3
km
3
.
Permutation Tests
Authors: Hallin Marc , Ley Christophe 
Published: January 2013

Abstract
Permutation tests are an interesting and conceptually simple alternative to traditional tests when the required distributional assumptions (typically, Gaussian assumptions) are likely to be violated. We start with three motivational examples explaining how to construct permutation and sampled permutation tests, and provide empirical evidence of the way they outperform their traditional competitors onseveral counts: validity, unbiasedness, and power. We then show how permutation tests follow from more traditional Neyman alpha‐structure arguments, and discuss some asymptotic results establishing that extended validity, in this context, does not imply any loss of asymptotic efficiency.
Air Quality Index, Facilitating Communication With
Authors: Hunt Jr William F. 
Published: January 2013

Abstract
Air quality indicators can take many different forms. Why are they needed? We live in a technical society that collects vast quantities of environmental data. Indicators translate this environmental data into a form that is readily understood. Those asking questions seek answers in the simplest form. Is the environmental problem becoming better or worse? Often the questioner is told the problem is “too complex.” The governmental official or scientist may prefer to give no answer at all than give an imperfect answer that could lead to a misunderstanding. The layman prefers an imperfect answer to no answer at all. An indicator must be able to translate complex information into a form that is readily understood by decision makers as well as laymen. Indicators may have many uses: daily reporting to the general public (for those pollutants with short‐term health effects), for trends, and for evaluating the overall effectiveness of environmental policies/regulations across multiple pollutants. This article discusses the development of the Pollutant Standards Index (1976), which was promulgated as the index used for public reporting in 1979 and was later updated to become the USEPA's Air Quality Index (1999).
Repeated Measures
Authors: Kenward Michael G. 
Published: January 2013

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.
Resampling Methods
Authors: Tu Wanzhu 
Published: January 2013

AbstractThis article presents an overview of existing resampling methods and their applications in parameter estimations and statistical inference. It describes the essential concepts and basic operating procedures of the most frequently used resampling methods, including the jackknife, bootstrap, permutation test, and cross‐validation. This article also discusses the interconnections among these methods. Finally, it briefly reviews the currently available software for implementation of the most commonly used resampling procedures.
Royal Statistical Society, Environmental Statistics Section (Formerly Study Group)
Authors: El‐shaarawi Abdel H. 
Published: January 2013

Abstract
The group was formed in 1996 and then became a full section, the Environmental Statistics Section, of the Royal Statistical Society in the United Kingdom. Its objectives are to act as a national focus for statisticians in the United Kingdom involved in environmental work and to provide a cadre of experts who can be called upon for professional advice.
Sample Support
Authors: Goovaerts Pierre 
Published: January 2013

AbstractSample 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.