bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonparametric Regression Model
Authors: Opsomer Jean D. 
Published: September 2006

AbstractNonparametric regression is a rapidly growing and exciting branch of statistics, both because of recent theoretical developments and because of more widespread use of fast and inexpensive computers. Many methods are currently available, including kernel‐based methods, regression splines, smoothing splines and wavelet and Fourier series expansions. This article introduces the main types of smoothing methods and discusses the usefulness of nonparametric regression for analyzing datasets, particularly of the types encountered in environmental statistics.
Nuclear Risk
Authors: Grimston M. 
Published: September 2006

Abstract
The concept of ‘risk’ is inherently linked with that of uncertainty. In the field of energy, uncertainty manifests itself in a number of areas. For example, there is considerable uncertainty about future demands for energy. It seems highly likely that global energy demand will continue to grow rapidly for some decades, but credible projections for energy use in the year 2050, say, can vary by a factor of 2 or more depending on assumptions made. Even more uncertainty surrounds the atmospheric effects of burning fossil fuels. In order to stabilize carbon dioxide concentrations in the atmosphere at double preindustrial levels, it would be necessary to reduce emission of the gas by 60% but global emissions in 2010 are projected to be 30% higher than in 1990. The risks of non‐fossil sources of electricity production such as nuclear power and renewables need to be judged against such a background.
Occupancy Problem
Authors: Mcdonald Trent L. 
Published: September 2006

AbstractThe ‘occupancy problem’ has also been called the ‘sequential occupancy problem’ and the ‘coupon‐collector's problem’. Although there exist many variations, one popular statement of the problem asks, ‘What is the probability that a set ofnindependent multinomial trials yield at least one member ofyclasses?’ The answer to this question has direct application in many sampling situations, particularly when sampling for members of a population with certain characteristics (such as species members, members with defects, etc.). An answer to this question, under general probabilities of class membership, is given in this article through the occupancy probability mass function and cumulative mass function. This article also mentions the corresponding waiting‐time question that asks how many multinomial trials need to be conducted (i.e. how large shouldnbe) in order to have a certain chance of obtaining at least one member ofyclasses.
Official Statistics, Environmental
Authors: Ross N. Phillip 
Published: September 2006

Abstract

Official statistics can be defined as any set of data produced by a governmental agency. This article addresses some of the important statistical issues relating to the generation and use of ‘official statistics’ on the environment. The majority of environmental statistics is generated by governmental agencies and is
official
. The environmental data collected are usually for the purpose of developing standards and measuring compliance to those standards; however, they are often used for generating a number of secondary statistics that are also considered
official
statistics. A number of statistical issues arise in the generation of official environmental statistics. The remainder of the article focuses on some of the major issues and the approaches being taken to deal with them.
Optimization
Authors: Smyth Gordon K. 
Published: September 2006

Abstract
Optimization means to find that value which maximizes or minimizes a given function. The idea is intimately connected with statistical methods such as least squares, maximum likelihood, posterior mode and so on. Many optimization algorithms are based on the idea of solving the nonlinear equation that arises from setting the derivative vector equal to zero. Except in linear cases, optimization almost invariably proceeds by iteration. This article is concerned with unconstrained optimization in which function arguments not specially restricted.
In general, optimization methods which use derivatives are more powerful than those that do not, although the increase in speed may not always outweigh the overhead in computing the derivatives. If first and second derivatives of the function are available, Newton's method is simple and works well. Some sort of backtracking strategy, such as a line‐search or Levenberg–Marquardt damping, is necessary, though, to prevent divergence from a poor starting value. If second derivatives are not available, then quasi‐Newtonian methods, of which Fisher's method of scoring is one, are recommended. Particular applications of Fisher scoring to nonlinear regression and to generalized linear models are described. General purpose quasi‐Newtonian algorithms build up a working approximation to the second‐derivative matrix from successive values of the first derivative. If even first derivatives are unavailable, the Nelder–Mead downhill simplex algorithm is compact and reasonably robust. However, the slightly more complex direction‐set methods or Newtonian methods with finite‐difference approximations to the derivatives should minimize most functions more quickly. The one‐dimensional problem is considered separately. In one dimension, once one can provide an interval that contains the solution, there exist efficient ‘low‐technology’ algorithms robust enough to take on all problems.
A maximum or minimum of the function can be either global or local, and various heuristics have been used to distinguish one from the other. Any method that relies on the iterative refinement of a single working approximation will fail in the presence of a large number of local optima. Algorithms that are specially designed to cope these circumstances include simulated annealing and genetic algorithms.
Organizations, Environmental Statistics
Authors: Marker David A. 
Published: September 2006

Abstract
Environmental statistics is by its very nature an international endeavor. Every nation in the world must address environmental issues ranging from pollution assessment to biodiversity, global warming to ecosystem monitoring. To solve these issues, any one nation must cooperate with one or more other nations. It is therefore not surprising that many international organizations have formed to work on solutions to environmental statistical issues. This entry provides brief summaries of the major environmental statistical organizations. The list is not intended to be all‐inclusive, rather to provide a short introduction to some of the primary sources for professional information on environmental statistics.
Ozone
Authors: Cocchi Daniela , Trivisano Carlo 
Published: September 2006

Abstract
Ozone gas is produced by photochemical dissociation of atmospheric oxygen as a result of shortwave solar ultraviolet radiation. It is highly concentrated in the stratosphere, mainly between 15 and 40 km. Stratospheric ozone is necessary for life on Earth, since it absorbs solar ultraviolet radiation, which should have mutagenetic effects on living tissues of plants and animals. In recent years it has been discovered that the ozone layer is thinned in the ozonosphere above Antarctica. This has been caused mainly by chlorofluorocarbures (CFCs) from human activities, which are emitted into the stratosphere. This phenomenon, commonly known as the ‘ozone hole’, is different from the increase of ozone in the troposphere, which is the lowest part of the atmosphere near the earth’s surface, i.e. a thin stratum of atmosphere which on average occurs at 10 km altitude. The chemical and biological processes that regulate life on earth, and most of the meteorological processes, occur in the troposphere.
P Value
Authors: Delampady Mohan 
Published: September 2006

AbstractThePvalue is one of the tools from classical statistics that is used very widely in applied sciences. It is also one of the concepts that is highly misunderstood and misused. The basic idea behind Fisher's original (1925) definition ofPvalue did have a great deal of appeal: it is the probability under a (simple) null hypothesis of obtaining a value of a test statistic that is at least as extreme as that observed in the sample data.
Plant strategies
Authors: Fang Wei 
Published: September 2006

Abstract
Plant strategies are a complex set of approaches that plants use to maximize their success in particular environments or under particular conditions. These strategies can be characterized by identifiable suites of specific traits. The search for plant strategies is inspired by a long‐standing desire of ecologists to seek simplified patterns in plant diversity and the complexity of ecosystems. The effort to define and quantify plant strategies will clarify patterns of the geographical distribution of flora, enhance generalizations about their physiological, phenological and ecological functions, and improve predictions about ecosystem sensitivity to changes in the environment. Accurate descriptions of plant strategies will also help in synthesizing results from individual studies and enhance our understanding of ecological patterns and processes at landscape and global scales. This article emphasizes descriptions of plant strategies and the development of associated quantitative approaches.
Point Processes, Temporal
Authors: Brillinger David R. , Guttorp Peter M. , Schoenberg Frederic Paik 
Published: September 2006

Abstract

A temporal point process is a random process whose realizations consist of the times {τ

j

}, τ

j

∈ ℝ,
j
= 0, ±1, ±2, … of isolated events scattered in time. A point process is also known as a counting process or a random scatter. The times may correspond to events of several types.
Point Processes, Spatial
Authors: Guttorp Peter M. , Brillinger David R. , Schoenberg Frederic Paik 
Published: September 2006

AbstractThe general theory of point processes has usually been focused on temporal developments. The special case of events that occur in space (or, more precisely, on a plane) has developed some particular approaches, usually assuming spatial stationarity and isotropy (i.e. the distribution of the process is invariant under translations and rotations, respectively). This article focuses on this type of process. Points, or events, take place at spatial locations, and each event is identified with its location.
Pollutant Measurements
Authors: Mole Nils , Palmgren Finn , Zhang Hao 
Published: September 2006

Abstract
Measurements of pollutant concentrations are needed in monitoring to determine when an emergency response is required, for example close to installations that store or process hazardous chemicals; to determine whether legal standards are complied with; and to gain knowledge and understanding that can be used to improve modeling, prediction and mitigation of the effects of pollution. This article deals with measurement techniques and strategies appropriate to major pollutants in both air and water, and also with the effects of unavoidable measurement errors.
Proportional Hazards Model
Authors: Piegorsch Walter W. 
Published: September 2006

AbstractA short introduction is given into construction and use of the proportional hazards model with time‐to‐event data. Applications include environmetric settings where analysis of the survival pattern(s) from a set of observations is studied. The presentation includes material on a form of distribution‐free proportional hazards model, known as the Cox regression model.
Radioactivity
Authors: Scott E. Marian 
Published: September 2006

AbstractRadioactivity is present in all components of the environment, it has two main sources: the first is natural, created at the time of the Earth's formation, the second is anthropogenic, created more recently as a result of man's activities. The presence of radioactive elements in the environment has stimulated a great deal of research on the distribution, transport and biological effects of environmental radioactivity. Statistical methods have an important role to play in many areas of the scientific study of radioactivity, including construction of sampling plans, monitoring and mapping, predictive modeling (in the presence of uncertainty), and risk assessment.
Random Field, Gaussian
Authors: Worsley Keith J. 
Published: September 2006

Abstract

The
Gaussian
random field
Y
(
t
),
t
∈
T
, is one of the most common models used to describe spatial stochastic processes. In many applications, the domain
T
is a subset of
D
‐dimensional Euclidean space (usually
D
= 2 or
D
= 3), and the function
Y
(
t
) is almost surely continuous or smooth in
t
. The definition is simple: the Gaussian random field must be multivariate Gaussian at all finite sets of points, that is, [
Y
(
t
1
), …,
Y
(

t
n

)] must be multivariate Gaussian for all
n
> 0 and all

t
j

∈
T
. Since the multivariate Gaussian is specified uniquely by its mean vector and variance matrix, then the Gaussian random field is defined uniquely by its mean function
μ
(
t
) = E[
Y
(
t
)] and its covariance function
C
(
s
,
t
) = cov[
Y
(
s
),
Y
(
t
)].
Randomized Branch Sampling
Authors: Valentine Harry T. 
Published: September 2006

Abstract
Randomized branch sampling (RBS) is a special application of multistage probability sampling, which was developed originally by Jessen to estimate fruit counts on individual orchard trees. In general, the method can be used to obtain estimates of many different attributes of trees or other branched plants. The usual objective of RBS is the estimation of a total quantity for a tree or a branch, for example, woody dry matter or volume, leaf dry matter, or counts of tree‐inhabiting insects. RBS also has potential application in the estimation of attributes of river networks and other branched structures.
Ranked Set Sampling
Authors: Patil G.P. 
Published: September 2006

Abstract
Environmental monitoring and assessment typically requires observational data, as opposed to data obtained from controlled experiments. Obtaining such data requires identification of sample units to represent the population of concern, followed by selection of particular units to quantify the characteristic(s) of interest. Sample units are generally the smallest possible units of measurement, such as plots, soil cores, individuals, etc., while typical characteristics of interest include biomass, chemical concentrations or ‘head counts’. Typically the most expensive part of this process is laboratory analysis, while identification of potential sample units is a comparatively simple matter. One can therefore achieve great observational economy if able to identify a large number of sample units to represent the population of interest, yet only have to quantify a carefully selected subsample. This potential for observational economy was recognized for estimating mean pasture and forage yields in the early 1950s when McIntyre proposed a method, later coined ranked set sampling (RSS) by Halls and Dell, and currently under active investigation in various quarters.
Record Linkage
Authors: Winkler William E. 
Published: September 2006

Abstract

Record linkage is the methodology of identifying pairs of records associated with entities from two or more files. Entities may be persons or businesses. Record linkage also refers to finding duplicates within files. Sometimes, records are linked using unique identification numbers such as a Social Security Number. Typically, non‐unique identifiers such as names, addresses, and other information are used. These methods of record linkage are called exact matching. Methods for exact matching may be based on
ad hoc
rules that may be applicable in a limited number of situations. Methods may also be based on a formal mathematical model as discussed in this article.
Regression Trees
Authors: Leblanc Michael 
Published: September 2006

Abstract
Regression trees are a flexible statistical method for modeling the conditional distribution of a response variable given a vector of predictors or covariates. Trees have several advantages compared with classical parametric regression methods: they are completely nonparametric; they include powerful variable subset selection; they are robust to outliers in the covariate space and are used easily on a wide variety of data structures.
Regularization Methods
Authors: Lukas Mark A. 
Published: September 2006

Abstract
Over the last 40 years there has been rapid growth in the field of inverse problems. In general terms, a direct problem is to find the effect from some cause using a completely specified model. For an inverse problem, the effect is known (in practice, usually imprecisely as measured data), and it is desired to find either the cause or some component of the model itself. Environmental applications of inverse problems occur in different types of remote sensing, image restoration, chemical sorption, ecology, and hydrology. Unlike direct problems, inverse problems are usually ill‐posed. A problem is well‐posed if a solution exists, is unique, and depends continuously on the data. An ill‐posed problem is inherently difficult to solve in practice since, even if it has a unique solution, the solution is sensitive to errors in the data. In order to obtain a reasonable approximate solution, it is essential to stabilize the problem using some method of regularization.
Reproductive Toxicology
Authors: Williams Paige L. 
Published: September 2006

Abstract
Reproductive toxicology is the study of adverse effects of environmental agents on the male and female reproductive systems, including reduced fertility, menstrual disorders, spontaneous abortion, developmental defects including low birth weight, and genetic diseases affecting the reproductive system and offspring. In particular, there is concern regarding the high rate in humans of pregnancy, congenital malformations, and serious developmental defects possibly due to environmental factors. Also of specific interest is an apparent increase in male sexual effects such as reduced sperm production and testicular cancer. Public health concern regarding these reproductive issues has been heightened by an increase in the number of women in the workforce during their childbearing years, and by the large increase in numbers of couples seeking treatment for infertility. While the cause of many of these adverse reproductive outcomes is unknown, at least some proportion can be attributed to known environmental agents.
Residuals
Authors: Lee Alan J. , Seber George A.F. 
Published: September 2006

AbstractMany scientific problems involve modeling the relationship between variables. Regression analysis is concerned with modeling the behavior of a variableY(the response or dependent variable) in terms of a setx1, …,xpof explanatory variables (also called covariates, regressors, and independent variables). The model takes the form E(Y) = f(x1, ,xp; β) wherefis one of a ‘sensible’ family of functions, and β is a parameter vector which must be estimated from the data. We also need to postulate the statistical distribution ofyfor a fixed covariate vector x = (x1, …,xp)T. The assumed form offis tentative and we need various diagnostic checks to assess its suitability. To do this, we need a measure of the discrepancy between each datumyand the ‘fitted value’ŷpredicted by the model. These measures are known asresiduals. The precise form of the residual depends on the type of data (continuous data, counts, binary data) and the form of the model.
Respiratory Epidemiology
Authors: Coull Brent A. , Schwartz Joel 
Published: September 2006

AbstractEnvironmental health scientists now recognize respiratory illness as a potential health effect in both children and adults. Respiratory effects may include asthma, wheezing, altered pulmonary function in the form of respiratory flow or volume, dyspnea (shortness of breath), and bronchitic or chronic bronchitic symptoms. Risk factors having documented associations with respiratory illness or disease include maternal smoking, environmental tobacco smoke, respirable particulate matter, ozone, nitrogen dioxide, and acid aerosols. Other studies have focused on possible protective effects of diet against lung disease in humans. This article focuses on some statistical challenges often encountered in the analysis of respiratory data.
Ripley'sKFunction
Authors: Dixon Philip M. 
Published: September 2006

AbstractRipley’sK(t) function is a tool for analyzing completely mapped spatial point process data, i.e. data on the locations of events. These are usually recorded in two dimensions, but they may be locations along a line or in space. This article will only describeK(t) for two‐dimensional spatial data. Completely mapped data include the locations of all events in a predefined study area. Ripley’sK(t) function can be used to summarize a point pattern, test hypotheses about the pattern, estimate parameters and fit models. Bivariate or multivariate generalizations can be used to describe relationships between two or more point patterns. Applications include spatial patterns of trees, herbaceous plants, bird nests and disease cases.
Risk Assessment, Probabilistic
Authors: Carrothers Timothy J. , Thompson Kimberly M. , Burmaster David E. 
Published: September 2006

Abstract
A primary criticism of environmental risk assessment as it has been practiced in the last 20 years has been the failure of analysts to properly account for issues of variability and uncertainty. Many risk assessments focused on producing ‘point estimates’ of risks have provided risk managers and decision makers with information that is at best incomplete and at worst dangerously misleading. As the field of risk assessment has matured, techniques for better treatment of variability and uncertainty have been developed and incorporated into what is known as ‘probabilistic’ risk assessments. In contrast to the point estimate approach, where single values are assumed for each model input, the probabilistic approach quantifies the possible range of values that each input may take, as well as the probability associated with each value. Modern computational power then allows the analyst to generate a multidimensional distribution of risk that reflects both the variability of risk across different individuals in the population as well as the uncertainty associated with each estimate.
Risk Assessment, Quantitative
Authors: Stern Alan H. 
Published: September 2006

AbstractTwo different forms of risk assessment are commonly in practice. They have somewhat different orientations and address different regulatory and communications goals. One form of risk assessment can be referred to as safety assessment. This type of assessment seeks to identify the largest level of exposure or dose that can reasonably be expected to be without adverse effect. It does not necessarily provide quantitative information on the risk associated with exposure above this ‘safe’ level. The other type of risk assessment seeks to quantify the risk associated with any exposure or dose of an environmental agent. This latter form of risk assessment is the focus of this article.
Robust Inference
Authors: Hampel Frank 
Published: September 2006

AbstractRobust inference is inference that is insensitive to (smaller or larger) deviations from the assumptions under which it is derived. Some very commonly used assumptions in statistics are normality, independence, identical distributions, linearity (e.g. in regression), and stationarity of stochastic processes. As a rule, such assumptions are only approximations to reality, and the questions arise as to what deviations tend to occur in practice, what effects they have on known statistical procedures, and how to develop better, ‘more robust’ procedures when deemed desirable. To answer these questions, new statistical concepts have proven useful.