bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geographic Information Systems (GISs), Spatial Statistics in
Authors: Cressie Noel 
Published: September 2006

AbstractGIS is a networked collection of hardware and software tools that recognize georeferencing in the data and feature the georeferencing in database management, data analysis, and visualization of results (principally maps) from the analysis. This combination of geography and computer science has recently become known as geographic information science. Since data locations are usually an important part of any environmental study, the use of GIS in environmental sciences is growing rapidly.Spatial analysishas become the accepted term within the GIS community for any data analysis that involves the georeferential aspects of the data. This entry is concerned with spatial statistical methods, which make up quite a small fraction of the many GIS functionalities that are spatial in nature.
Gibbs Sampling
Authors: Storvik Geir 
Published: September 2006

AbstractStochastic simulation is an important part of statistical computing as well as a much‐used numerical tool for calculating integrals in other fields (Monte Carlo integration). For low‐dimensional problems inversion/transformation or rejection sampling can be applied for simulating from a specific distribution. When working with real problems, simulation from high‐dimensional distributions often becomes necessary. Gibbs sampler is an algorithm for simulation in high‐dimensional spaces.
Hazardous Agents
Authors: Urfer Wolfgang 
Published: September 2006

AbstractEvaluation of hazards associated with exposure to chemicals, understanding of relationships between dose and adverse effect, extrapolation of effects from high experimental doses to low doses associated with actual exposures, and extrapolation from effects observed in animals to effects expected in humans are the main issues of statistical research in risk assessment of hazardous agents. Statistical aspects of inhalation toxicology, proof of hazard, genetic toxicology and the role of oncogenes in carcinogenesis are discussed. Finally, the need for more statistical research to identify hazardous agents by DNA and protein sequence analysis is outlined.
Hierarchical Bayes Methods
Authors: Ghosh Malay 
Published: September 2006

AbstractDose–response studies of environmental toxins are designed to determine the detrimental effects associated with exposure to the toxin, over a particular range of exposures. To properly quantify the toxic effect of a toxin, thebackground riskof no exposure to the toxin, R(0), must first be specified. The additional risk incurred by exposure to a dose,d, of the toxin is then called the added risk (AR) at this dose and is given by AR(d) = R(d) –R(0).
Hierarchy Theory, Ecological
Authors: Allen Timothy F.H. 
Published: September 2006

Abstract
Hierarchy theory is a dialect of general systems theory, and as such is particularly concerned with the role of the observer in choosing criteria for prescribing or describing levels. In simple ecosystems there is an accepted set of values. In complex systems, the very structure changes as new levels emerge and the system invites the observer to assess the situation at any of a large number of levels. In the face of complexity, the myth of the objective observer fails because structure can melt into process, meaning can disappear, and qualitative distinctions change into quantifiable matters of degree. These fundamental changes come about as the complex system matures, decays, or reorganizes, or as the observer shifts her level of analysis. Even when the change has a material basis, the observer is still responsible for deciding difference. How much difference must there be before the system is estimated to be unstable or a new structure is recognized? Thus hierarchy theory focuses on the observer's decisions, which makes it particularly adept at dealing with complex systems.
Hydrological Extremes
Authors: Burn Donald H. 
Published: September 2006

AbstractAn accurate estimation of the probability of occurrence of an extreme hydrological event is often required for the effective development and management of water resources. Examples include characterizing extreme rainfall event magnitudes to predict urban runoff, determining river flow magnitudes to design a culvert or bridge opening at a road crossing, or determining the likely severity of a drought event during the design life of a water supply system. In each of these examples, an understanding of the hydrological response of a watershed is essential in order to obtain a reliable estimate of the relationship between extreme hydrological event magnitudes and the probability of these events occurring.
Incomplete Block Designs
Authors: Federer Walter T. , Nguyen Nam‐ky 
Published: September 2006

AbstractThe subject of statistics deals with variability and how to deal with it. In the planning and conduct of an environmental or ecological investigation, the items used to control variability are (a) refinement of experimental technique, (b) selection of homogeneous material and/or environments, (c) grouping (blocking, stratifying) material into homogeneous subgroups (blocks, strata), and (d) measurement of related variables and use of covariance. There are many ways of blocking (arranging) the experimental units (EUs) in a comparative experiment withvtreatments. If the sample of EUs is from a homogeneous population, then no blocking is required and a completely randomized experiment design (ED) of thevtreatments randomly allotted to thervEUs is used. The replicate number (sample size) for each treatment isrunless unequal replication is desired. If homogeneous blocks of sizevare available to accommodate allvtreatments, a randomized complete block ED (allvtreatments in each block, not necessarily an equal number of times) is used. In many situations, the block size,k, is less thanvand an incomplete block ED (not all treatments in each of the blocks) is used.
Intercropping, Statistical Issues In
Authors: Federer Walter T. 
Published: September 2006

Abstract

Intercropping is defined as the simultaneous or sequential growing of more than one crop on the same area of land. It may involve intermixing the plants of the crops, growing them in alternate rows, or growing them sequentially through time. When dealing with a cropping system, it is necessary to study the response for each component of the mixture (when available) as well as for the combined data for the mixture. This will involve statistical procedures not found in standard statistics textbooks. A popular statistical procedure for analyzing results from experiments involving several intercropping treatments is a relative yield ratio or land equivalent ratio (LER). The yields of the individual crops in a mixture need to be available in order to compute a LER. An LER is defined as the sum of the ratios of the yield of the
i
th crop in a mixture to its yield as a sole crop.
Landscape Pattern Metrics
Authors: McGarigal Kevin 
Published: September 2006

Abstract
Landscape ecology is largely founded on the notion that environmental patterns strongly influence ecological processes. The habitats in which organisms live, for example, are spatially structured at a number of scales, and these patterns interact with organism perception and behavior to drive the higher‐level processes of population dynamics and community structure. A disruption in landscape patterns may therefore compromise this structure's functional integrity by interfering with critical ecological processes necessary for population persistence and the maintenance of biodiversity and ecosystem health. For these and other reasons, much emphasis has been placed on developing methods to quantify landscape patterns, which is considered a prerequisite to the study of pattern–process relationships. This has resulted in the development of literally hundreds of indices of landscape patterns.
Line‐Transect Sampling
Authors: Cowling Ann 
Published: September 2006

Abstract
Line‐transect sampling methods have been used since the 1930s to estimate the abundance of many biological populations, such as animal, bird, and plant species, and even nonliving things. In a line‐transect survey, an observer moves along a transect line and notes the location of all animals detected relative to the line. It is assumed that not all animals are detected. Factors affecting detectability include distance from the line, habitat, and weather conditions. In the conventional approach to line‐transect sampling, it is assumed that all animals on the line are detected, and that the probability of detection decreases with distance from the line.
Logarithmic Regression
Authors: El‐shaarawi Abdel , Viveros(‐aguilera) Román 
Published: September 2006

AbstractLog regression (LR) is widely used to model environmental data because the measurements tend to show positively skewed frequency curves and higher variability with higher concentration levels. A compelling reason for the popularity of LR is the simplicity of estimating and making inferences about the regression coefficients using existing software packages for the normal theory linear models.
Lognormal Distribution
Authors: El‐shaarawi Abdel 
Published: September 2006

AbstractThe lognormal distribution is widely used by environmental scientists to model their data. Its popularity is based on the fact that many environmental measurements are non‐negative and their histograms are frequently skewed to the right but the histograms of log‐transformed data resemble those of the normal distribution. Another reason for its popularity is the simplicity of performing the analysis using the familiar normal theory methods. It has been shown that the lognormal distribution arises naturally by applying the central limit theorem when a multiplicative process is used to combine the error.
Longitudinal Studies
Authors: Gotway Crawford Carol A. 
Published: September 2006

Abstract
A longitudinal study is one in which the subjects under investigation are observed repeatedly over time. The same variable is measured on the same subject for at least two time periods. The subjects may be individuals or pre‐existing populations of interest (e.g. species, countries, watersheds). Replication necessary for estimation and inference is provided by the assumption that the subjects respond independently.
Long‐Term Environmental Monitoring
Authors: van der Maarel E. 
Published: September 2006

Abstract
Monitoring (from the Latin monere = to warn) is the act of following or controlling a process through a monitor and being warned if the process deviates from expectation. Environmental monitoring is the controlling of an environmental process, or ‘the intermittent surveillance to ascertain the extent of compliance with a predetermined standard or degree of deviation from an expected norm’. This means in practice a series of carefully planned, repeated observations of parameters indicating changes in a process we do not want to change. Monitoring ‘provides the essential data on how systems are changing and how fast’.
Markov Chain Monte Carlo (MCMC)
Authors: Jones Galin L. , Hobert James P. 
Published: September 2006

AbstractRealistic statistical models often give rise to probability distributions that are computationally difficult to use for inference. Fortunately, we now have a collection of algorithms, known as Markov chain Monte Carlo (MCMC), that has brought many of these models within our computational reach. MCMC is a simulation technique that allows one to make (approximate) draws from complex, high dimensional probability distributions. Over the last 10 years a staggering amount of research has been done on both the theoretical and applied aspects of MCMC. This article does not intend to be a complete overview of MCMC but only hopes to get the reader started in the right direction. To this end, this article begins with a general description of the types of problems that necessitate the use of MCMC. It then introduces the fundamental algorithms and addresses some general implementation issues. Any discussion of the Markov chain theory underpinning MCMC algorithms, have intentionally been avoided.
Markov Random Field Models
Authors: Kaiser Mark S. 
Published: September 2006

AbstractStatistical models formulated on the basis of Markov random fields (MRFs) constitute an appealing approach to the analysis of data that exhibit complex dependence structures. Such data arise commonly in problems of a spatial or spatiotemporal nature, although MRFs are a general mathematical construct that may be used in other situations such as, for example, the representation of genetic dependence among organisms. Within the context of statistical modeling, a MRF may be defined for any set of random variables defined at a discrete point.
Maximum Likelihood Estimation, Nonregular
Authors: Holland David M. 
Published: September 2006

AbstractFor bounded probability distributions capable of J or U shapes, the log‐likelihood function can become unbounded as the boundary parameters approach their respective limits. However, use of penalty function techniques that urge the maximum likelihood (ML) search away from the unbounded region can be used to circumvent this problem. Another approach to nonregular problems is ‘correcting’ the likelihood to estimate the model parameters. In an application to simulated data, this technique was found to be effective with quick convergence to parameter estimates.
Meteorological Adjustment of Air Quality
Authors: Thompson Mary Lou 
Published: September 2006

Abstract
Poor air quality impacts adversely on the health of humans and ecosystems. Air pollutants result both from natural sources and from human activity. Oxides of carbon, nitrogen and sulfur are, for instance, products of combustion activity, such as that generated by motor vehicles. Other secondary pollutants such as ozone result from reactions in the atmosphere between primary pollutants and other chemicals known as precursors. The dispersion of airborne pollutants and the reactions among them are influenced by meteorological conditions. This article describes some of the existing approaches to meteorological adjustment of air quality, i.e. the modeling of (some aspect of) air quality that takes account of (adjusts for) meteorological conditions. In doing so, this article will also highlight challenges that exist and raise issues that need further attention.
Environmental Microbiology
Authors: Hamilton Martin 
Published: September 2006

Abstract
Micro‐organisms are critical to life on earth. They are at the very bottom of the food chain, providing the nutrients required to sustain other forms of life. Scientists are only now discovering the extent to which micro‐organisms control broad biochemical processes, such as the cycling of carbon, nitrogen, and sulfur. Bacteria live in all habitats accessible to any form of life – from Antarctic lakes to deep oceanic hot water vents – even 2000m below the earth's surface, where no fresh organic input has been experienced for millennia. Rigorous investigation of environmental issues must recognize, and take account of, microorganisms.
Missing Data
Authors: Hopke Philip K. , Liu Chuanhai , Rubin Donald B. 
Published: September 2006

Abstract

A common problem with environmental data is that there may be samples or sampling intervals for which there are no data. Environmental studies cannot be designed for experiments that can be reproduced, therefore a sample that was not taken or was lost cannot be recovered. These losses of samples represent a total loss of information about the content of the species that were not directly determined for that sample. Also often there are only sufficient resources to perform partial sampling (every
x
th time interval) instead of contiguous measurements, or to analyze only part of the samples collected. This leads to values that are not obtained, and no information is available about the variables of interest during these sampling intervals. Incomplete data makes analysis using standard complete‐data methods like distribution fitting impossible. Filling in missing values has strong appeal, because then standard complete‐data methods can be applied and existing software can be used without any modification. This general strategy reduces greatly the burden of developing methods and computer codes for analyzing incomplete data.
Model Uncertainty
Authors: Chatfield Chris 
Published: September 2006

Abstract

The modeling of complex environmental stochastic systems is a difficult task. There may be little or no background theory, the data may be of doubtful quality, and it is not always clear what the objectives are. Nevertheless, scientists have made substantial progress in getting a better insight into environmental relationships and changes. However, much remains to be done, especially in regard to the thorny topic of model uncertainty. Although it is widely recognized that all models are
approximations
and need to be modified and improved as more information becomes available, many scientists continue to make predictions from models as if they really believe them to be true. As a result, the accuracy of out‐of‐sample predictions is typically (much) worse than would be expected from the within‐sample fit.
Model‐Based Inference
Authors: Chambers Ray 
Published: September 2006

AbstractA basic problem in survey sampling is that of using the sample data for a survey variableYto infer the value of the totalTofYover the sampled population. The model‐based approach to this problem is to base this inference on a joint statistical model for both the population values ofYand, where appropriate, for the sampling method. This is in contrast to design‐based sampling inference, where the only source of uncertainty is the outcome of the sampling method.
Modeling, Environmental
Authors: Solow Andrew R. 
Published: September 2006

AbstractIn discussing environmental models, it is useful to distinguish between process‐based or mechanistic models and phenomenological or empirical models. Although the distinction is not always clear, mechanistic models are based on a scientific theory about the underlying environmental processes, while empirical models are based on observation alone. This article focuses on statistical analysis under mechanistic environmental models. This is not to imply that empirical environmental models are less interesting or less useful. Rather, it is because empirical environmental models are, from a statistical perspective, no different from empirical models in other fields. While the methods of statistical analysis under mechanistic environmental models are certainly not unique, such models tend to share certain characteristics that determine in part the kind of statistical issues that arise, and even the kind of statistical methods used to address these issues.
Multivariate Data Visualization
Authors: McLeod A.I. , Provost S.B. 
Published: September 2006

AbstractMultivariate data visualization is an exciting area of current research by statisticians, engineers and those involved in data mining. This article first gives a brief overview of the most important current software available for data visualization and then discusses the general principles or philosophy involved in data visualization. This is followed by a discussion of some of the most interesting and useful multivariate graphical techniques.
Natural Disasters
Authors: Davenport A.G. , Kopp G.A. 
Published: September 2006

Abstract
Natural disasters are the interaction of naturally occurring hazards with human population. Natural disasters are often defined in social and economic terms, one common definition being that the community involved cannot recover without external help. Meteorologist Joseph Golden of the National Oceanic and Atmospheric Administration says that ‘a hazard only becomes a disaster when it occurs where people live’. Where people live is an important aspect of natural disasters because it defines the vulnerability to natural hazards. According to Flageollet, ‘vulnerability is often assessed in terms of the probable cost of the damage the hazard has done or may do’. The risk of a natural disaster is a combination of both the hazard and the vulnerability to the hazard.
Natural Resources Modeling
Authors: Rathbun Stephen L. 
Published: September 2006

Abstract
Environmental resources are biotic, including fish and game populations, timber, and grasslands, or abiotic, including water, air, and soil. Interest may focus on either the quantity of available resources (e.g. volume of available timber, or size of fish populations), or their quality (e.g. the concentration of contaminants in air, water, or soil samples). Environmental resource models can lead to a better understanding of environmental processes, and assist managers in making decisions regarding sustainable levels of resource exploitation, remediation of impaired habitats, and the maintenance of biodiversity. This article describes univariate and multivariate methods for predicting the data at unsampled sites from observations at sampled sites. Univariate models are used when the values of the explanatory variables are available at all sites for which predictions are desired. A multivariate approach is required when the explanatory variables are measured only at a sample of sites. For both approaches, models for the spatial correlation structure of the data are considered, including both parametric and nonparametric forms. Methods for model fitting and spatial prediction are described.
Nearest Neighbor Methods
Authors: Dixon Philip M. 
Published: September 2006

AbstractThis entry discusses nearest neighbor (NN) methods for the analysis of spatial point patterns and the analysis of field experiments. The distance from a point to its NN, or related quantities, can be used to test whether the observed locations are a realization of a specific spatial point process. Edge effects, caused by recording data only within a bounded study area, can be important. When locations are marked (e.g. by the species of plant), NN methods provide information on the relationship between the marks. NN methods for field experiments can be used to model local spatial variabiltiy.
Nonlinear Regression
Authors: Smyth Gordon K. 
Published: September 2006

AbstractThe basic idea of nonlinear regression is the same as that of linear regression, namely to relate a response to a vector of predictor variables. Nonlinear regression is characterized by the fact that the prediction equation depends nonlinearly on one or more unknown parameters. Whereas linear regression is often used for building a purely empirical model, nonlinear regression usually arises when there are physical reasons for believing that the relationship between the response and the predictors follows a particular functional form. The unknown parameters in the regression function are typically estimated by minimizing the residual sum of squares, although other criteria can be used if it is desired to protect against non‐normal errors. Popular nonlinear regression models include sums of exponential functions, rational functions or growth curves such as logistic or Gompertz curves. The estimates usually have to be computed iteratively by using numerical optimization methods such as the Gauss–Newton algorithm. Good‐quality algorithms usually incorporate step‐length modifications designed to ensure a reduction in the residual sum of squares at each step. In some cases, the complexity of the fitting process can be reduced by taking advantage of conditionally linear parameters. Inference for nonlinear regression is classically based on analogies with linear regression using the gradient matrix from the Gauss–Newton algorithm, but recent work on curvature measures has led to new diagnostics and higher‐order approximations.