bluuebunny/crossref_metadata_2025 huggingface dataset:
Historical Controls
Authors: Ryan Louise
Published: September 2006

Because development toxicity, carcinogenicity, and other toxicological studies are typically conducted in genetically homogeneous animal strains, the large historical database that builds up over time can often prove useful in interpreting results from current experiments. For instance, if a certain defect has only rarely appeared among control animals, then its appearance among even a few exposed animals may indicate strong cause for concern even though statistical tests may not yield a significant dose effect for that current study. Of course, the use of historical control information requires caution. It is important that the current and historical studies are similar with respect to study design, animal species, quality assurance and review procedures, as well as diagnostic and nomenclature conventions.
H
otelling's
T
‐Squared Statistic
Authors: Gupta A.K.
Published: September 2006

The T
2
statistic is used to test hypotheses about the population mean vector
μ
and to obtain confidence region for
μ
when the covariance matrix Σ is unknown.
Image Analysis
Authors: Glasbey C.A. , Horgan G.W.
Published: September 2006

The term
image analysis
means the use of computers to interpret digital versions of images. Such methods are of increasing relevance in all sciences, including environmetrics. Image analysis is a broad, interdisciplinary field with many challenging problems to which statistical methods are applicable A digital image usually consists of a rectangular array of pixels, each of which measures the brightness of a part of the original scene, where ‘brightness’ may denote reflected light but could equally represent any other variate that has been measured on a 2‐dimensional grid. Methods of analysis range from low‐level processing of individual pixels or small neighborhoods, such as the use of filters to enhance images, to segmentation, the spatial clustering of pixels into segments that represent regions or objects, or to high‐level processing, where the information in whole images is summarized.
Impact Assessment
Authors: Stewart‐Oaten Allan
Published: September 2006

A common monitoring problem is: Describe the effect of an environmental alteration on the abundance of a given species at the ‘Impact’ site. Harbors, breakwaters, developments, sewage outfalls, oil platforms, coastal power plants and recreational access – and removals or redesigns of these – are examples in the marine environment, and plants, invertebrates and fish are typical populations of concern. The Impact site is defined naturally in some cases, as a bay or estuary, but arbitrarily in others, as a region surrounding the alteration. Before permitting such alterations, decision makers review predicted effects. These are often subjective, widely varying, and wrong. Monitoring and assessment can aid later decisions: to close the alteration down, modify its design or operation, require mitigation or compensation, allow expansion, or collect further data. By exposing error, it helps keep predictors honest; by adding information, it improves future decisions.
Indicator Species
Authors: Farr Daniel
Published: September 2006

The problem of deciding what to monitor to gauge progress towards biodiversity conservation has been the focus of considerable discussion. Three approaches are prominent: habitat indicators, indicator species, and biodiversity indicators.
Informatics, Environmental
Authors: Tochtermann Klaus
Published: September 2006

Environmental informatics is the science that deals with the development and application of methods and tools for environmental information processing. The main objectives of environmental informatics are to make the best possible use of information technologies: (a) to contribute to an effective and efficient protection of the environment; (b) to provide the public with information on the state of the environment and, thus, to increase environmental awareness; (c) to support environmental planning; and (d) to forecast future states of the environment.
Inhalation Toxicology
Authors: Sen Pranab K.
Published: September 2006

Inhalation toxicology relates to the composite assemblage of ambient toxins with respect to their prevalence, uptake sites and intake process by inhalation, mode of biological, molecular and carcinogenic reactions, and consequential health hazards. From a statistical perspective, the major task is to arouse our awareness of potential and asserted toxic substances, to gather scientific evidence of their impact on human life and on our ecosystem as a whole, and to appraise (statistically as well as epidemiologically) the risk due to the aggregation of such toxic and carcinogenic (overt or not) effects of our own bioenvironment. This risk assessment has emerged as an interdisciplinary one, where toxicology, molecular biology, pharmacology, epidemiology, and environmental health sciences all have major shares; nevertheless, statistics plays a key role in this crucial assessment task.
International Biometric Society
Authors: Billard Lynne
Published: September 2006

The International Biometric Society plays a leading role in the advancement of biometry and biostatistics at the international level by fostering the exchange of new ideas and relevant methodologies for a wide range of separate disciplines, including the important areas of environmental biology and ecology.
Inverse Prediction
Authors: Brown Philip J.
Published: September 2006

Inverse prediction concerns predicting one set of measurements from another set when the direction of causation and possibly error operates in the opposite direction. An example is that of calibration in which it is desired to use a cheap and quick but error‐prone measurementY, to predict the true amount of a constituentx, which in itself can be measured accurately in laboratory conditions but at much greater effort or cost. After taking data on samples using both measurements (the training or learning data) it is desired to use the cheap measurement in future.
Iteratively Reweighted Least Squares
Authors: Jorgensen Murray
Published: September 2006

Iteratively 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

The 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.
J
ournal of
A
gricultural,
B
iological, and
E
nvironmental
S
tatistics
Authors: Young L.J.
Published: September 2006

JABES
strives to develop the interface between statistics and the biological sciences with an emphasis on agriculture, biotechnology, the environment, and natural resources.
Kriging
Authors: Switzer P.
Published: September 2006

A 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

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.
Landform Monitoring
Authors: Carn S.A.
Published: September 2006

Landform monitoring concerns both the description of landforms and landform systems, and modeling of the processes that act on them.
Conditional Entropy Profiles
Authors: Patil G.P.
Published: September 2006

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

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

Developed 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

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.
Long‐Range Dependence
Authors: Robinson P.M.
Published: September 2006

Long‐range dependence, or
long memory
, usually refers to a strong correlation between distant observations in a time series. Evidence of long‐range dependence has been found in diverse fields, such as geophysics, agriculture, chemistry, economics and finance.
M
arkov Chains
Authors: Nitcheva Daniela
Published: September 2006

Markov chains are sequences of random variables, usually denoted by {

X
n

,
n
 = 0, 1, 2, …} with a special dependency structure.
Markov Process, Hidden
Authors: Zucchini Walter , Macdonald Iain L.
Published: September 2006

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

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

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
.
Meteorological Extremes
Authors: Simiu Emil
Published: September 2006

This article discusses briefly basic principles of extreme value (EV) estimation, the role of simulations for the development of relevant meteorological data sets, the interaction between the practical application of meteorological data and the extreme value modeling process, and specific EV estimation issues pertaining to wind, snow, ice, and ocean waves.
Meteorology
Authors: Thomson David J.
Published: September 2006

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

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.
Model‐Assisted Sampling
Authors: Hoef Jay M. Ver
Published: September 2006

Model‐assisted sampling is concerned with making design‐based inference using model‐based estimators to improve precision.
Morbidity and Mortality
Authors: Hernán Miguel A.
Published: September 2006

The frequency of overall or cause‐specific morbidity and mortality is employed in national and international statistics to characterize the health status of a population.
Morphometrics
Authors: Reyment Richard A.
Published: September 2006

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

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

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

Kriging 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

In 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

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

The 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

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

Nested 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

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

Feed‐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

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

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

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

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

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.
Outliers, Multivariate
Authors: Barnett V.
Published: September 2006

Environmental problems frequently involve simultaneous consideration of several relevant random variables. Often, it is the occurrence of extreme values of the random variables that is of principal concern. In a univariate random sample the notion of an extreme value, as a smallest or largest observation, is well defined. The corresponding concept for multivariate data is less accessible, since such data cannot be ordered unambiguously. Extreme values can provide naturally interpretable measures of environmental concern in their own right. If they are not only extreme, however, but ‘surprisingly extreme or unrepresentative’ then they may suggest that some unanticipated influence is present in the data source. Such extremes are known as outliers. For general multivariate data there is no natural concept of an extreme, and preliminary
outlier detection procedures
will almost always be needed to reveal outlying data points before one can progress to analyzing them and taking appropriate action (rejection, accommodation, identification) in respect of them.
Pattern Recognition
Authors: Stein Alfred
Published: September 2006

Pattern recognition is defined as the automatic identification of figures, characters, shapes, forms and patterns without active human participation in the decision process. These can be structures in one dimension, for example an array of characters, a time series or any other signal, or a structure in two dimensions, such as an image. Higher dimensional patterns are rarely if ever encountered. Pattern recognition can be further divided into recognizing existing features that have a well‐known effect on an image, or the search for coherent subpatterns that may have a common cause. A pattern therefore consists either of features that are known to exist, but are somehow difficult to identify, or of features that may get a specific meaning afterwards (i.e. after they are detected).
Permutation Tests
Authors: Russek‐Cohen Estelle , Jackson Monica
Published: September 2006

Permutation tests are a class of nonparametric methods for hypothesis testing. These methods are related to randomization tests.
Personal Exposure Monitoring
Authors: Liu L.‐j. Sally
Published: September 2006

Exposure to a pollutant occurs when an individual encounters the specific pollutant. The science of exposure assessment is to assess how much, how long, and how frequently an individual is exposed to the compounds of interest. Accurate and reliable exposure‐related information is essential for informed decisions about protecting and promoting public health.
Point Processes, Dynamic
Authors: Gamerman Dani
Published: September 2006

This topic deals with the specification and estimation of the intensity rate of point processes that allows for arbitrary number of changes of level. The change points can be specified online as the process is observed or in advance. Recent techniques allow for the estimation of number and time location of changes.
Point Processes, Spatial–Temporal
Authors: Schoenberg Frederic Paik , Brillinger David R. , Guttorp Peter
Published: September 2006

A spatial–temporal point process (also called space– time or spatio‐temporal point process) is a random collection of points, where each point represents the time and location of an event. Examples of events include incidence of disease, sightings or births of a species, or the occurrences of fires, earthquakes, lightning strikes, tsunamis, or volcanic eruptions. Typically the spatial locations are recorded in three spatial coordinates, e.g. longitude, latitude, and height or depth, though sometimes only one or two spatial coordinates are available or of interest.
Poisson process
Authors: Çinlar Erhan
Published: September 2006

Point processes are used as models for the configurations formed by the random positions of some species of particles in some space; the terms ‘particle’ and ‘position’ take different meanings in different applications. If the particles can be treated as indistinguishable, it is convenient to represent the point process by a random measure
N
, letting
N(A)
denote the number of particles in the set
A
, with
A
ranging over a suitable collection of subsets of the space involved. Poisson random measures (PRMs) (or processes) are the point processes par excellence: they are mathematically tractable and are applicable in a wide range of situations, either directly or after some transformations.
Pollutant Standards Index
Authors: Hunt William F.
Published: September 2006

Air quality indicators can take many different forms. The question that needs to be answered is: ‘Why are they needed?’. Indicators are needed to translate this environmental data into a form that is readily understood by decision‐makers, as well as by laymen. In order to accomplish this, different indicators are proposed for different pollutants or combinations of pollutants. These air quality indicators can play an important communications role. Some of the questions that will be addressed in this entry are: Can an air quality index be misleading? Can it mask significant changes in pollutant trends? What information do you lose when you combine multiple pollutants into a single index? How do you deal with these problems? Does the solution lie in the development of multiple air quality indicators and indices? This entry will attempt to address these questions for daily air quality reporting and for the purposes of assessing air quality status and trends.
Population Dynamics
Authors: Quinn Terrance J.
Published: September 2006

Population dynamics is the study of the characteristics of a population and how they change over time and space. These characteristics include abundance (population size in numbers), biomass (population size in weight), age, sex, growth, reproduction, death, human‐induced factors such as hunting and fishing, and other life history traits. Mathematics and statistics are the natural languages for population dynamics. Change is the province of differential and difference equations. Populations structured by age, length, and/or area lend themselves to treatment with matrices and linear algebra. Resolving the factors affecting a population requires data collection using sampling theory and studies involving experimental design. Population models integrate information from a variety of sources with biological principles and mathematical structure. There is never enough information to understand fully a population and what affects it, so contemporary modeling emphasizes uncertainty by incorporating stochasticity.
Population Ecology
Authors: Dixon Philip M.
Published: September 2006

Population ecology is the discipline in ecology that deals with the structure and dynamics of biological populations. The focus may be on a single population in isolation, or one of a few interacting populations. Population ecology has a long history of using quantitative models to connect theory and data. Some of these models are mathematical; they are often used to explore the behavior of populations that follow simplified mathematical rules. Others are statistical; they are often used to estimate population quantities – e.g. survival rate – or compare different models. Because the topic is vast, this entry only summarizes major uses of statistical models in population ecology.
Population Health Surveillance
Authors: Lawson Andrew B.
Published: September 2006

The need for surveillance of population health is fundamental to public health intervention, and within this role, there is also a need to provide functional capacity for data collection and analysis. This latter area should involve relevant statistical analysis of health outcomes. Within the intersection of environmental and epidemiological studies the application of health surveillance within environmental epidemiology is clearly a core focus. The monitoring of diseases, which have an aetiological link with environmental agents, is of great concern. However, surveillance methodologies are underdeveloped within statistical approaches to spatial and/or temporal environmental epidemiology.
Population Viability Analysis
Authors: Ver Hoef Jay M.
Published: September 2006

Population viability analysis is the application and study of extinction effects in ecological population modeling. Population viability analysis has important applications for studying the global and local extinction of populations. It attempts to identify threats to populations and then evaluate the probability that the population will persist to some future time.
Precision Agriculture
Authors: O'Halloran I.P.
Published: September 2006

Precision agricultural systems attempt to optimize the overall productivity of a field by spatially (and sometimes temporally) matching the level of input or management to the requirements of the crop.
Prior Distribution
Authors: Gelman Andrew
Published: September 2006

The
prior distribution
is a key part of Bayesian inference and represents the information about an uncertain parameter
θ
that is combined with the probability distribution of new data to yield the
posterior distribution
, which in turn is used for future inferences and decisions involving
θ
. The existence of a prior distribution for any problem can be justified by axioms of decision theory; this article focuses on how to set up a prior distribution for any given application.
Quantiles
Authors: Allen O. Brian
Published: September 2006

Quantiles are defined for a sample ofnobservations and for the probability distribution of a population of observations. In many cases, the sample quantile may be viewed as an estimate of the corresponding quantile for a population. Some of the most commonly used quantiles have special names. The first, second and third quartiles divide a probability distribution into four equal parts. Thus, 25% of observations are less than the first quartile and 25% of observations are greater than the third quartile. The second quartile is better known as the median. Other quantiles with special names include the quintiles, which divide a distribution into five equal parts, and percentiles, which divide a distribution into 100 equal parts. Quantiles play an important role in environmental science in a number of ways, examples of which are given in this article.
Quasi‐Likelihood
Authors: Moore Dirk F.
Published: September 2006

Quasi‐likelihood methods are procedures for model fitting that are partially parametric in the sense that one need only specify the first two moments (i.e. the mean and variance) of the distribution of the responses. Quasi‐likelihoods may be used in place of likelihoods when it is not practical or convenient to specify a full probability model for the data.
Radon
Authors: Ginevan Michael E.
Published: September 2006

This article discusses radon, the second leading cause of lung cancer after cigarette smoking. Statistical issues discussed include estimation of the form of the dose–response function relating lung cancer risk to radon exposure, and the radiation dose that one might receive from a given level of radon exposure.
Random Fields
Authors: Adler Robert J.
Published: September 2006

The term ‘random field’ is, in general, used to describe a random process defined over a region ofN‐dimensional Euclidean space, whenN > 1. The earliest examples studied in any detail in the statistical literature were actually agricultural yields in plots of land (N = 2) from whence, presumably, came the terminology. The second class of examples, which lead to a far deeper understanding of the spectral properties of space–time fields, was that of temporally evolving sea surfaces (N = 3).
Random Number Generators
Authors: Mccullough B.D.
Published: September 2006

Random number generators (RNGs) serve many important uses in statistics: bootstrapping, Monte‐Carlo, simulation, and numerical integration of distribution functions are just a few examples. In most applications the random numbers are generated from an algorithm and so are technically calledquasi‐ orpseudo‐random numbers, because such numbers are not random at all, but deterministic. However, what matters in applications is that the numbers appear to be random. Therefore, as long as the pseudo‐random numbers appear to be random, the fact that they are calculated deterministically is largely irrelevant.
Receptor Modeling
Authors: Henry Ronald C.
Published: September 2006

Practical and economical plans to reduce air pollution require models that relate the sources of airborne pollution to concentrations at sampling sites. In receptor models, samples of airborne pollution are subjected to extensive chemical analysis. Contributing sources leave chemical fingerprints in the sample. The amount of pollution coming from each source can be estimated if the chemical fingerprints of the sources are known. Thus, receptor models do not require any knowledge of emission rates or meteorological observations that are very important to atmospheric dispersion models. However, receptor models do rely on the observed concentrations of chemical species in the atmosphere. These species must be conserved during transport between the source and the receptor. Chemical elements are good candidates for receptor modeling since these are often immutable under ordinary conditions and relatively easily measured.
Reduced‐Rank Regression
Authors: Aldrin Magne
Published: September 2006

Reduced‐rank regression is a multivariate linear regression method where several response variables are related to the same set of predictor or explanatory variables, and where the estimated matrix of regression coefficients is of reduced rank.
Regression Diagnostics
Authors: Davison Anthony C.
Published: September 2006

Statistical diagnostics are used to help data analysts detect and find the source of any problems that may have arisen with the application of statistical procedures, and if possible to suggest remedies. Often they have two elements: a graph constructed to highlight potential difficulties and a numerical test statistic intended to help gauge their significance. Regression involves studying the dependence of one or a few variables on others and is perhaps the most widespread statistical activity. Consequently, regression diagnostics have been well studied and are incorporated into most statistical packages. Moreover, the general ideas that underlie them are useful much more widely, with close links togoodness of fitandrobustness.
Remote Sensing
Authors: Campbell K.
Published: September 2006

In this article, remote sensing refers to the acquisition of information from digital geospatial data acquired from an overhead perspective, using sensors that sample and record electromagnetic radiation in one or more regions of the electromagnetic spectrum that is reflected or emitted from the surface of the Earth. The ultimate goal of remote sensing is to extract information from remotely sensed data about the material properties of the Earth's surface and of the atmosphere, together with their geographical relationships. However, the remote measurements are not controlled exclusively, or even directly, by the variables of interest at the surface of the Earth. Therefore, an understanding of the physical basis for remote sensing and the characteristics of remote sensing data is necessary to guide the modification of statistical and other standard methods for extracting information from data. Generally several preprocessing steps are required before such methods can be applied. These include the correction of geometric and radiometric distortions in the data as well as feature extraction for more efficient data processing.
Repeated Measures
Authors: Kenward Michael G.
Published: September 2006

A repeated measures study is distinguished by the collection of a sequence of measurements from each unit over a period of time. Repeated measures can be collected in both experimental and observational settings; examples of the latter are often referred to as longitudinal studies. The sequence of measurements from one unit is sometimes called the unit's
profile
. These profiles form a collection of short time series, and one should expect the measurements from the same unit to be mutually correlated.
Reproductive Epidemiology
Authors: Cho Sung‐Il , Wang Xiaobin
Published: September 2006

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