bluuebunny/crossref_metadata_2025_split huggingface dataset:
Iteratively Reweighted Least Squares
Authors: Jorgensen Murray 
Published: September 2006

AbstractIteratively reweighted least squares (IRLS) is an algorithm for calculating quantities of statistical interest using weighted least squares calculations iteratively. IRLS algorithms may be simply implemented in most statistical packages with a command language because of their use of standard regression procedures. In addition to the required coefficients IRLS produces weights for each observation that aid in understanding the behavior of the algorithm during the iterations. They are also useful diagnostics for identifying unusual data once convergence has been reached.
Jackknife Resampling
Authors: Friedl Herwig , Stampfer Erwin 
Published: September 2006

AbstractThe jackknife is a particular resampling method that aims primarily at the calculation of the bias and the variance of estimates, without making very restrictive distributional assumptions. It is nonparametric and easy to apply in quite general settings. As with other resampling techniques, the jackknife can be computationally very intensive. In the simplest case jackknife resampling is generated by sequentially deleting single cases from the original sample (delete‐one jackknife). A more generalized jackknife technique uses resampling that is based on multiple case deletion (delete‐djackknife). The main part of this article focuses on independently and (in the introductory part also) identically sampled data. It then discusses the basics of the jackknife and its applications to nonidentically distributed observations that can be described by linear, generalized linear, nonlinear, and Cox's regression models. Finally, the article gives an account of jackknife estimates for dependent data, e.g. in the context of generalized estimating equations or special time series models.
Kriging
Authors: Switzer P. 
Published: September 2006

AbstractA spatial field is partially observed at selected sites and the goal is to infer the field at unobserved sites. This is the problem of spatial estimation, sometimes called spatial prediction. Examples of spatial fields are soil nutrient concentrations over an agricultural domain observed on a survey grid, hydrologic variables over an aquifer observed at well locations, and air quality measurements over an air basin observed at monitoring sites. Kriging is a method for converting the data into an estimate of the field together with a measure of error or uncertainty.
Lakes
Authors: Imberger Jörg 
Published: September 2006

Abstract
The understanding of lake hydrodynamics has made much progress in the past 20 years and a broad, holistic overview now exists. However, it is still difficult for the general limnological practitioner to gain a quantitative description of the hydrodynamical regimes active in a particular lake at a particular time. Such an overview is important because the mixing and transport processes operating in a lake determine, to a large degree, the ecological response of the lake to meteorological forcing, inflows and outflows. In the field of hydraulics a well established methodology exists, based on the Reynolds and Froude numbers, which allows characterization of flows into laminar or turbulent and super‐ or subcritical. Once classified in this way, many attributes of the flow immediately follow. In this article, the author attempts to provide an analogous first order methodology that will allow the practitioner to classify the hydrodynamic regimes operating in a lake. Such a classification scheme should allow the limnologist to put his or her ecological data into an appropriate physical context. This contribution considers the parameterization of the dynamics of the surface layer, the internal wave regimes, the inflow dynamics, the behavior of a selective withdrawal layer, and mixing in the interior of the lake.
Conditional Entropy Profiles
Authors: Patil G.P. 
Published: September 2006

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 amongst 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.
Law and Environmental Statistics
Authors: Gastwirth Joseph L. , Nayak Tapan K. 
Published: September 2006

Abstract
Many environmental regulations and standards are based on statistical data and inferences drawn from them. Often the statistical analysis used by the US Environmental Protection Agency (EPA) or other governmental body, in developing an exposure or effluent limit or a monitoring program, is challenged by the industry or public interest group that is most affected by its decision. Then, the courts need to examine the record on which the EPA or other agency relied in setting the standard. Because there are several statutes regulating the environment in the US as well as in Europe, in assessing the adequacy of the underlying scientific support of a regulation or protocol, one needs to be aware of the relevant statute. This article discusses several important statistical issues that have arisen in actual cases and briefly describe some other topics that arise in this area.
Least Squares
Authors: Haslett John 
Published: September 2006

AbstractDeveloped about 200 years ago, the method – more formally, the criterion – of Least Squares (LS) seems likely to remain at the heart of the statistician's toolbox for the twenty first century. Virtually every text on elementary statistical methods gives some exposition and it lies at the heart of very many algorithms routinely used in modern data analysis, most notably in regression. To avoid confusion, only the criterion and the method more formally known as Ordinary Least Squares (OLS) are discussed in this article. As OLS is so strongly associated with the statistical modeling procedure of linear regression, it is appropriate to include an introduction to regression under the heading of OLS. In addition, the solution of the numerical equations associated with this model has almost become synonymous with OLS, and therefore the solution itself is also discussed in this article, as are some extensions.
Long‐Term Ecological Research (
LTER
) Program
Authors: Gosz James R. , Waide Robert 
Published: September 2006

Abstract
The network dedicated to Long‐Term Ecological Research (LTER) in the US has grown to 24 sites since it was formed in 1980. In addition to adding sites, the focus of the network has shifted from a concentration on individual and site research to a broader network and synthetic viewpoint. The goal of these latter studies is to search out general ecological principles that apply to many ecosystems at many different scales.
Markov Process, Hidden
Authors: Zucchini Walter , Macdonald Iain L. 
Published: September 2006

Abstract

Hidden Markov processes, more commonly known as hidden Markov models (HMMs), are a class of models for time series {

X
t

,
t
= 0, 1, 2, …} in which the probability distribution of

X
t

is determined by the unobserved (‘hidden’) state

C
t

of an
m
‐state Markov chain {

C
t

,
t
= 0, 1, 2, …}.
Maximum Entropy Sampling
Authors: Lee Jon 
Published: September 2006

Abstract

The goal of maximum entropy sampling is to choose a most informative subset of
s
random variables from a set of
n
random variables, subject to side constraints. A typical side constraint might be a budget restriction, where one has a cost for observing each random variable. Other possibilities include logical constraints (e.g. multiple choice or precedence constraints). In many situations, one can assume that the random variables are Gaussian, or that they can be suitably transformed.
Measurement Error
Authors: Stefanski L.A. 
Published: September 2006

Abstract

Statistical models are often defined in terms of variables X that are not directly observable. In such situations it is common for substitute variables W to be observed instead. In some cases the variable W is a measurement of X with nonnegligible error. For example, the measured distance W that an animal lies from a transect line can differ significantly from the true distance X However, in other cases the relationship between W and X is not as simple as that between measurement and measurand. In all cases X is preferred over W for most statistical analyses: standard methods for estimating animal abundance from line‐transect data assume distances are measured without error. However, X is not observable for each observational unit, whereas W is. Statistical analyses that are standard and straightforward when X is observed are generally neither when W is observed in place of X. The problem is to fit statistical models described in terms of X (and other observed variables) given data on W (and other observed variables). Problems of this type are commonly called measurement error problems, and the statistical models and methods for analyzing such data are subsumed under the heading
measurement error modeling
.
Meteorology
Authors: Thomson David J. 
Published: September 2006

Abstract
Meteorology is the study of the atmosphere. Despite the fact that the weather has been a central concern of humanity since very early in its history, it was only with the development of extensive observing networks in the twentieth century that a reasonably complete picture of the atmosphere and its behavior began to emerge. The reason for this is that, although some aspects of the weather can be understood as local phenomena, many aspects can only be understood on a large scale or even in a global context. In addition, observations of the ‘upper air’ can often be as important in understanding the behavior of the atmosphere as those near the surface. As a result, the development of meteorology has been dependent crucially on the international cooperation needed to make, collate, and exchange meteorological observations. The earth now has a large network of observations comprising surface‐based observations, upper air measurements from balloons, radiosondes and aircraft, and more recently observations from remote sensing techniques involving satellites and radar, and much of the resulting data is widely exchanged around the world in near real time.
Mixed Outcomes
Authors: Molenberghs Geert , Geys Helena 
Published: September 2006

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.
Morphometrics
Authors: Reyment Richard A. 
Published: September 2006

Abstract
Morphometrics is defined as the application of multivariate statistical procedures to the study of variation in morphological characteristics of organisms. As originally implemented, the analyses are based on standard measures (‘distances’) spanning critical sites. A more recent introduction is concerned with quantifying outlines of organisms, the data then being a suite of arbitrary coordinate‐pairs around the circumference of the object, obtained by some automatically operating digitization procedure (e.g. the Zahn–Rosskies method). Geometric morphometrics is the latest class to appear. It relies on diagnostically located ‘landmarks’ that form the basis for superimposition of coordinate systems, visualizations by deformation grids. The first and third of these categories can, without particular difficulty, be allied with environmental indicators by appropriate techniques.
Multiple Comparisons
Authors: Edwards D.G. 
Published: September 2006

Abstract

In comparing several unknowns, e.g. the means
μ
1
, μ
2
, …, μ
k
of
k
> 2 populations, the classical approach would recommend starting with a test of equality, i.e. test the hypothesis H
0
:
μ
1
= μ
2
= … = μ
k
. The process known as multiple comparisons has historically been considered as the logical next step following a rejection of this ‘omnibus’ test: if we can be confident that some differences among the

μ
i

exist, then
which
pairs μ
i
,

μ
i

’
are different (1 ≤
i
≠
i
′ ≤
k
) and (perhaps) how large are these differences? Modern multiple comparisons analysis retains the goal of answering these sorts of questions, though the preliminary test of equality is no longer sacrosanct.
Multiple Endpoints
Authors: Pocock Stuart J. 
Published: September 2006

Abstract

In any experimental or epidemiologic study, there may be several disease or response outcome measures that one wishes to relate to markers of environmental exposure and/or other relevant risk factors. Such
multiple endpoints
may well be justified scientifically since one often cannot anticipate exactly which measure of response will be affected by exposure. The statistical problem is that one then runs the risk of data dredging and distortive reporting with a post hoc emphasis on the most statistically impressive findings.
Multivariate Kriging
Authors: Wackernagel H. 
Published: September 2006

AbstractKriging is a special type of optimala linear prediction applied to random processes in space, i.e.random functions, with the particular requirement that the covariance structure of the random function should be known. Kriging can be extended to several variables by specifying a corresponding system of direct and cross covariance functions to be used incokriging. Alternatively, secondary variables known at all sample and prediction locations can be included into kriging or cokriging asexternal drift–ignoring the cross covariances with the primary variable.Multicollocated cokrigingis an alternative to the external drift method, taking advantage of simplifications in the cokriging system derived from assumed proportional effects in the covariance structure.
Mutagenesis, Environmental
Authors: Piegorsch Walter W. 
Published: September 2006

AbstractIn experiments studying the effects of environmental stimuli on biological organisms, data are analyzed via various statistical approaches. Each of these depends on the nature of the biological endpoint and aspects of the particular assay under study. Among major endpoints of interest, damage to genetic components such as deoxyribonucleic acid (DNA) is of increasing interest. Mutagenesis is perhaps the most varied of any toxicological endpoint, since the potential mechanisms of genetic damage are so numerous. Statistical issues associated with mutagenicity experiments include construction of models to characterize dose response and other features of genetic damage, sample size determinations/study design, and the selection of statistical approaches associated with these models and methods.
N
ational
I
nstitute of
S
tatistical
S
cience (
NISS
),
US
Authors: Karr Alan F. 
Published: September 2006

Abstract
The National Institute of Statistical Sciences (NISS) is a research institute, established in 1990 and located in Research Triangle Park, North Carolina, whose mission is to identify, catalyze and foster high‐impact cross disciplinary research involving the statistical sciences.
Negative Binomial Distribution
Authors: El‐shaarawi Abdel H. 
Published: September 2006

AbstractThe negative binomial distribution is widely used to analyze count data. In environmentrics, one is frequently interested in summarizing the changes in the abundance of particular organisms in the environment either for protection or impact assessment. For example, monitoring the safety of a drinking water source is based on the estimation of the density of an indicator bacteria from the number of organisms found in several water samples. Another example is the estimation of the abundance of threatened species and the impact of habitat changes on their numbers in ecological conservation.
N
elder
P
lots
Authors: Barry Ronald P. 
Published: September 2006

Abstract
A Nelder Plot, also called a Nelder Wheel or Nelder Fan, is a systematic planting design in which plants or trees are planted at the intersection of circular arcs and linear spokes. In general, Nelder Plots allow many different planting densities to be examined in a single plot. This is frequently more efficient than planting a different plot for each planting density. Nelder Plots can be constructed that allow the effect of different planting geometries to be examined in a single plot. The article discusses the construction of Nelder Plots and the analysis of data derived from Nelder Plots.
Nested Experimental Designs
Authors: Shah Kirti R. , Sinha Bikas K. 
Published: September 2006

AbstractNested experimental designs form an important subclass of experimental designs wherein some factors are found to be naturally nested within the ‘levels’ of other factors. In a study to examine differential effects of several methods of reducing water pollution, for example, water samples may be taken from different counties and in each county possibly from different water sources, say wells. These samples are then treated with the available methods (for reducing water pollution) and the resulting data analyzed. In this case we may consider the counties as being the levels of the first fac
Network Ecology
Authors: Christian Robert R. , Ulanowicz Robert E. 
Published: September 2006

Abstract

In network ecology one constructs and analyzes topological models to understand the intricacies of material and energy flows within ecosystems. These models (i.e. networks) consist of compartments (nodes) that contain a designated form of material or energy, the flows between these compartments, and those between the compartments and the external world. Emphasis is on the analysis of the constructed networks. The term that is most often associated with the practice of this discipline is
ecological network analysis (ENA)
. In actuality, the method is more than a single analysis. ENA includes several algorithms designed to aid the understanding of the flow structure of the network and, by inference, the corresponding ecosystem. These algorithms are particularly valuable to ecologists in categorizing compartments and groups of compartments, defining indirect relationships between compartments, and indexing system‐level attributes.
Neural Network
Authors: Hand David J. 
Published: September 2006

AbstractFeed‐forward neural networks are highly parameterized nonlinear models for function estimation. They had their origins in the 1960s, as the electronic revolution led to widely available computers. They originated in work on models of the brain (hence the name) but before long their potential for adaptive learning and updating was driving the research. Nowadays by far the greater part of both research and application is concerned with their potential as mathematical and computational tools for prediction, regardless of how well they model the ways in which biological brains may perform similar tasks.
Spatial Design, Optimal
Authors: Zimmerman Dale L. 
Published: September 2006

Abstract

Consider a situation in which a spatially varying quantity is to be observed at sites located in some region of interest. For example, measurements of groundwater contamination may be taken at various locations in an aquifer, or plant species richness may be observed on each of several plots in a meadow.
Optimal spatial design
refers to the determination of the number, dimensions, and spatial arrangement of the sites that optimize, in some sense, the information content of the observations. Typically, the optimization is subject to constraints on the set of candidate sites at which observations may be taken, and on the total cost of these observations. Political, ethical, and other considerations may also constrain the optimization, and hence statistical optimality may be only one aspect of a more comprehensive approach to spatial design.
Order Restricted Inference
Authors: Dykstra Richard , Kuo Hsun‐chih 
Published: September 2006

Abstract
A wide variety of statistical techniques have proven useful in biological and environmental studies. Frequently, these techniques can be improved upon and/or made more powerful if the prior knowledge that certain parameters satisfy an order restriction can be incorporated into the analysis. Moreover, since the existence and implications of orderings can be easily grasped and interpreted by even casual readers and observers, the ability to identify such structure can be especially important. Such structure may exist in many ways. Two possibilities that have been widely discussed are the presence of ordered parameters and the identification of ordered distributions.
Ornithological Data
Authors: Morgan B.J.T. , Viallefont A. 
Published: September 2006

Abstract
Ornithological data are collected in many different ways, and provide information on important aspects of animal behavior and demography, such as natural selection, movement and migration, memory, fecundity, mate selection, and survival. Birds reflect changes to the environment due for example to global warming or modifications in farming practices, and their scientific study is of clear importance, especially for conservation. Ornithological data may be collected as part of controlled scientific experiments, or may arise through the efforts of large teams of amateur volunteers. The latter is the case for large‐scale surveys, which are often characterized by problems of missing data. This article focuses on observational, rather than experimental, data.
Outcome‐Dependent Selection Models
Authors: Zhou Haibo , Weaver Mark 
Published: September 2006

Abstract

An
outcome‐dependent selection
(ODS) is a retrospective sampling scheme where one observes the environmental exposure/covariates with a probability that depends on the observed value of the outcome variable. It is an alternative to the more standard random sampling design. By allowing the selection probability of each individual in the ODS sample to depend on the outcome, the investigators attempt to enhance the efficiency and reduce the cost of the study. The case‐control study in epidemiology is a well‐known example of an ODS scheme with a binary outcome variable.
Outliers
Authors: Barnett V. 
Published: September 2006

Abstract
Extremes and outliers feature widely in the study of environmental issues. Often, the variable of natural interest is an extreme: the coldest temperature in the year, the highest ozone level, the weakest link in the bridge. Again, if one is interested in the whole range of values of an environmental variable, say when monitoring pollution levels in a coastal region, the extremes will still be important. In particular, if the highest pollution level is especially high it might indicate violation of some environmental standard. Alternatively, one may have reason to believe that it is too extreme to be a reflection of the intrinsic random variation, and that it indicates some abnormal event, e.g. a one‐off illegal discharge. Such an unrepresentative high value is called an (upper) outlier.