bluuebunny/crossref_metadata_2025_split huggingface dataset:
Outliers, Multivariate
Authors: Barnett V. 
Published: September 2006

Abstract

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

AbstractPattern 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).
Point Processes, Spatial–Temporal
Authors: Schoenberg Frederic Paik , Brillinger David R. , Guttorp Peter 
Published: September 2006

AbstractA 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

Abstract

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

Abstract
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

AbstractPopulation 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

Abstract
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

Abstract
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.
Prior Distribution
Authors: Gelman Andrew 
Published: September 2006

Abstract

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

AbstractQuantiles 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

Abstract
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.
Random Fields
Authors: Adler Robert J. 
Published: September 2006

AbstractThe 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

AbstractRandom 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

Abstract
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.
Regression Diagnostics
Authors: Davison Anthony C. 
Published: September 2006

AbstractStatistical 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

Abstract
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

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.
Reproductive Epidemiology
Authors: Cho Sung‐Il , Wang Xiaobin 
Published: September 2006

Abstract
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.
Resampling Methods
Authors: Friedl Herwig , Stampfer Erwin 
Published: September 2006

AbstractStatistical inference is based generally on some statistics (estimates) that are functions of the data. Their sampling distributions depend on the underlying population and therefore are unknown. Resampling methods offer strategies to estimate or approximate the sampling distribution of a statistic or its characteristics. These include accuracy measures of an estimate like its bias or the mean squared error, or relevant quantiles of a test statistic, among others.
Risk Assessment, Ecological
Authors: Halbrook Richard S. 
Published: September 2006

Abstract
An Ecological Risk Assessment (ERA) is a process that estimates the probability of an adverse effect on nonhumans. There are three major groups involved with the ERA process; the risk manager (responsible for making decisions), risk assessor (technical component of the assessment), and affected or interested parties. The major phases of an ERA are the problem formulation phase, analysis phase, and risk characterization phase. The end product of the ERA is a risk assessment report prepared by the risk assessor for the risk manager. The risk manager with use results of the ERA, along with economical, legal, and social factors in making risk management decisions and communicating with stakeholders regarding these decisions.
Prepared by: Richard S. Halbrook
Risk Assessment, Management and Uncertainties
Authors: Griffiths Richard 
Published: September 2006

Abstract

The need to understand the risks of an undertaking has led to the development of systematic methods of analysis for a number of areas of application. Some are concerned mainly or even solely with one kind of potential gain or loss, e.g. financial, whilst others are more obviously multidimensional, involving factors that are essentially incommensurate. Industrial impact on the environment is the focus of this article, and the term
industrial and environmental risk is used
to indicate the general scope. This article attempts to trace the development of the subject in such a way as to illustrate concepts and methods that have a general relevance across a number of specific areas of application. It is emphasized that risk is a multidimensional and dynamic concept that defies attempts to encompass its complexity within a single disciplinary framework, or to express it quantitatively on a single scale. The article emphasizes the need to be aware of the interaction between factual information and the value systems of stakeholders. Pitfalls of not maintaining this awareness are illustrated. The article aims to promote a critical approach to the use of risk assessment methods and concepts in pursuing the objectives of risk management.
Risk Perception
Authors: Everitt Brian S. 
Published: September 2006

Abstract
Life is a risky business, and deciding which risks are worth taking and which should be avoided has important implications both for an individual's lifestyle, and the way society operates. The benefits gained from taking a risk need to be weighed against the possible disadvantages. Acceptable risk is proportional to the amount of benefits. For the individual, living life to the fullest means achieving a balance between reasonable and unreasonable risk, and this will often be a matter of judgement depending largely on the individual's personality. However, in society as a whole, where the same balancing act is required, it has to be achieved through political action and legislation.
Robust Regression
Authors: Jorgensen Murray 
Published: September 2006

Abstract

Unusual observations can have a large effect on least squares fits of regression coefficients. The most common defenses against this problem are residual plots and the diagnostic statistics based on residuals and leverages. These tools are most effective when the dataset is moderate in size and the analyst is in close touch with, or is, the person who collected the data. However, regression diagnostics become very time consuming to use in large data sets. In this situation it may be preferable to use more
resistant
estimators that are relatively unaffected by even large changes to a small proportion of the data. In general, resistance must be ‘purchased’ at the cost of some loss of efficiency when the standard model assumptions hold, but these assumptions are seldom tenable in large data sets anyway. Regression diagnostics and robust regression are regarded by some as conflicting solutions to the problem of the sensitivity of standard least squares regression parameter estimates to unusual, possibly erroneous, observations. However, the two approaches can be seen as complementary. Residuals from robust fits can highlight interesting groups of observations not obvious from standard residual plots; leverage diagnostics can supplement the fits of some convenient, but only partially robust, regression methods.
S+Spatialstats
Authors: Vega Silvia C. , Kaluzny Stephen P. 
Published: September 2006

Abstract

S+SpatialStats is an add‐on module to the S‐Plus system for data analysis and graphics. S‐Plus is an environment for data analysis that provides an object‐oriented programming language and a multitude of functions for the statistical analysis of data. +patialStats provides a comprehensive suite of tools designed for the statistical analysis of
spatial
data. The current version, 1.5, features a graphical user interface (GUI), that is menus and dialogs designed for convenient access to the functionality described in this article.
Seismological Modeling
Authors: Rathbun Stephen L. 
Published: September 2006

AbstractThe precise assessment of the long‐term risk of major earthquakes is required for setting insurance rates, for establishing appropriate building codes, and for allocating resources to emergency management. The locations of earthquake epicenters’ together with the times at which they occur, form a spatiotemporal point pattern. If, in addition, their magnitudes (on the Richter scale) are available, then one has a marked spatiotemporal point pattern. For risk assessment, one shall assume that this pattern is a realization of a marked spatiotemporal point process, a random mechanism for generating the locations and times of events as well as their marks. The statistical approach to earthquake risk assessment depends on whether one is interested in assessing risk at a specific site (e.g. Los Angeles), or in assessing risk as a function of location. For the former, one shall consider models for the marginal temporal point pattern formed by the times of seismological events. Methods for fitting parametric models are described, and then the use of fitted models for assessing risk is considered. To assess risk as a function of location, the marginal spatial point pattern is considered. Here, a nonparametric approach is described for estimating the intensity of earthquake epicenters as a function of location.
Criticality, Self‐Organized
Authors: Li Bai‐Lian 
Published: September 2006

Abstract

The causes and possible relations between the abundance of fractal structures (
see
Fractal dimensions) and 1/
f
signals found in nature have puzzled scientists for years. Based on observations of computer simulations of the cellular automata sandpile model, the term self‐organized criticality (SOC) was invented by theoretical physicists and was used to explain a universal dynamic organizing process governing various fractal growths in space and time. SOC describes that systems organize themselves so their internal dynamics produce critical behavior over a very wide range of length scales. The term consists of two parts.
Self‐organization
has been used to describe the ability of certain nonequilibrium systems to develop structures and patterns often without explicit pressure or involvement from outside the systems.
Criticality
is a technical word used in connection with phase transitions, and has a very precise meaning, in which the system becomes critical when all members of the system influence each other and events of all sizes occur.
Sign Tests
Authors: Al‐saidy Obaid M. 
Published: September 2006

Abstract
The sign test is the oldest form of the distribution free tests, first introduced by Arbuthnot in 1710. Aside from its well‐established status, the sign test is easy to carry out and is applicable in a variety of situations. This article discusses the ordinary sign test for the median based on a single sample and on paired samples, and the Cox–Stuart sign test for detecting monotonic trend.
Simulation and Monte Carlo Methods
Authors: Storvik Geir 
Published: September 2006

AbstractSimulation involves using a model to produce results. The growing power of computers and the evolving simulation methodology have led to the recognition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Many applications of simulation are based on purely deterministic models. If the model contains a stochastic element, we have stochastic simulation, which will be the issue in this article. Stochastic simulation is often called Monte Carlo sampling, especially in engineering and physics literature.