bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drought
Authors: Wilhite Donald A. , Hayes Michael J. , Svoboda Mark D. 
Published: January 2013

Abstract
Drought is a naturally occurring event that is associated with virtually all climatic regions. There is no universal definition of drought since its characteristics and impacts vary by region and economic sector. Given its slow onset and other characteristics, including its spatial dimensions and duration, impacts are difficult to assess and have been, historically, poorly documented. These impacts are strongly influenced by a society's exposure to the hazard and the vulnerability of that society to the hazard. This vulnerability is continually changing in response to increasing population, land use changes, technology, government policies, and many other factors. Therefore, each drought event is superimposed on a society with differing vulnerabilities than existed when the previous drought event occurred. Drought impacts are increasing worldwide, both as a result of these changing vulnerabilities and, perhaps, because of an increase in the frequency, severity, and duration of drought events in association with a changing climate. To lessen societal vulnerability, it is imperative for nations to move away from crisis management approach to drought management and toward a more proactive, risk‐based approach. Emphasis must be given to the development of improved drought monitoring and early warning systems and the delivery of this information to decision makers at all levels. It is also essential that vulnerability assessments be conducted in order to determine who and what is at risk and why. A final step is the identification and implementation of appropriate mitigation measures or actions that will reduce future impacts on economic sectors and population groups.
Kriging for Functional Data
Authors: Giraldo Ramón , Mateu Jorge 
Published: January 2013

Abstract
Spatial functional data analysis is a relatively new topic in statistics. We have functional data when a random variable takes its values in a functional space and is observed at some sampling points. In this article, we focus on the case of functional data presenting spatial dependence, and present an overview of the existing methodology to make spatial predictions when the data values are functions. Kriging and its several related versions are widely known and used for spatial data. However, when the spatial data are functions, a bridge between functional data analysis and geostatistics has to be built. Ordinary kriging as well as cokriging and multivariate spatial prediction adapted to the case where the observations at each sampling location consist of samples of random functions are reviewed and compared.
Machine Learning
Authors: Vishwanathan S. V. N. 
Published: January 2013

Abstract

With the advent of modern technology, vast amounts of data are being collected in many fields, particularly in environmental and biological sciences. Machine learning, which is concerned with the design and development of algorithms that allow computers to extract useful patterns and trends automatically in order to make predictions, is a scientific discipline at the interface between statistics and computer. In this article, we focus on one particular learning paradigm, namely,
regularized risk minimization
, and show that it embodies some traditional statistical methods such as LASSO, linear regression, and kriging.
Geostatistics, Model‐Based
Authors: Zhu Jun , Thurman Andrew L. 
Published: January 2013

AbstractClassical geostatistics involves statistical inference for random fields with a continuous spatial index based on geostatistical data, but the methods tend to be ad hoc. Alternatively, likelihood‐based and Bayesian methods provide more efficient estimation of model parameters and more rigorous quantification of uncertainty in prediction of responses at unsampled locations.
Satellite Data
Authors: Shao Guofan 
Published: January 2013

AbstractEarth observation satellites collect data relevant to a wide range of land resource topics and there are a variety of satellite data that are designed for different purposes. Satellite data are collected, transmitted, stored, and analyzed in digital forms. Satellite data have a broad range of spatial, spectral, radiometric, and temporal characteristics. Commonly used terms are spatial, spectral, radiometric, and temporal resolutions. A general indication of advancement of satellite data is that their resolutions are getting higher over time. Satellite can be used alone, to derive new data, and integrated with other geospatial data sources. While most of commercial satellite data can be purchased from data providers, many governmental satellite data can be downloaded without charge from US government websites.
Screening Effect
Authors: Du Juan 
Published: January 2013

AbstractThis article reviews various applications and some theory of the screening effect, which describes the phenomenon where a spatial interpolant primarily depends on nearby observations in kriging. Justified by the screening effect, the nearest‐neighbor approach has been widely used in practice, mainly to reduce computational burden. However, caution needs to be exercised in choosing model and observation locations, since there may be no screening effect in some cases.
Singular Value Decomposition and High Dimensional Data
Authors: Allen Genevera I. , Perry Patrick O. 
Published: January 2013

Abstract
The singular value decomposition and related principal components analysis are foundational multivariate methods used for dimension reduction and exploratory analysis of high‐dimensional data. Studying this decomposition using random matrix theory, however, reveals troubling behavior when the number of variables is larger than the number of samples. This article surveys random matrix theory results related to the singular value decomposition and high‐dimensional data as well as recent developments such as sparse principal components analysis. Software on the random matrix theory distributions related to the singular value decomposition can be found elsewhere; several software packages implementing methods for sparse principal components analysis are also available.
Uncertainty and Computer Models
Authors: Sain Stephan R. 
Published: January 2013

AbstractWith the explosion of computing power in recent years, computer models have become important tools for scientists to study the behavior of different phenomena as well as for prediction and decision making. However, there are many sources of uncertainty in computer models that can have significant impact on what is learned from such models. The field of uncertainty quantification, which combines elements from statistics, applied mathematics, and computational science, is emerging to provide tools to better understand, quantify, and mitigate the many uncertainties in computer models, and this article covers some of the key issues and problems facing researchers in this area.
Fate and Transport Modeling
Authors: Raun Loren 
Published: January 2013

Abstract
The use of environmental contaminant fate and transport models stems from the need to predict a concentration of contaminant at a spatial location separate from the source at a specified temporal point. The models simulate a simplification of the real‐world scenario. A prediction of this sort is used to support decisions in a wide range of hazardous waste contaminant source scenarios, from small and localized to major industrial. The level of complexity of the model chosen is contingent upon the configuration of the source, the temporal question, the end use needs, and the availability of field data. The theoretical basis for the most common models, the methods used to solve the equations, and the general use of the models to predict the fate and transport of contaminants in groundwater are reviewed. Further, the assumptions, varying levels of model complexity, and sources of uncertainty are discussed.
Analysis of Variance, Multivariate (
MANOVA
)
Authors: Bilodeau Martin 
Published: January 2013

Abstract

A designed experiment is set up in which the system studied is under the control of an investigator. The individuals, the treatments, and the variables measured are decided by the investigator. In a clinical trial, patients satisfying some eligibility criteria are assigned at random, either to a new treatment or to a placebo. Patients are followed for some time and a few responses are recorded for each patient. In an agricultural experiment, a field is divided into plots of shape and size determined by the investigator. The plots are assigned at random one among a few treatments which could be, for example, different fertilizers. The variables measured on each plot could be the yields of some crops. The general objective of a designed experiment is to assess the effects of different treatments on the responses. These effects are evaluated by statistical estimates and confidence intervals of the magnitude of the differences between treatments. The estimates should avoid biases and the random errors should be minimized as much as possible. The statistical methodology used to analyze such designed experiments in which there are several responses is termed
MANOVA
, an acronym for multivariate analysis of variance.
Common Principal Components
Authors: Verdebout Thomas 
Published: January 2013

Abstract

The Common Principal Components model is a multivariate model in which the covariance matrices
Σ

i

,
i
= 1,…,
m
of
m
> 1 populations are assumed to have identical eigenvectors. Gaussian parametric inference methods are provided for common principal components. We present those Gaussian parametric tools and discuss several attempts to build robust testing methods for the null hypothesis of Common Principal Components.
Depth
Authors: Serfling Robert 
Published: January 2013

Abstract
Depth functions represent a recently emerging powerful methodology in nonparametric multivariate inference. They provide multivariate notions of order statistics and generate quantile contours, outlyingness functions, and sign and rank functions. There are wide possibilities for constructing such functions, but these narrow considerably when criteria such as affine equivariance or invariance, computational ease, asymptotic behavior, and robustness are applied. We review basic definitions, leading examples, key properties, and selected applications.
Factor Analysis, Dynamic
Authors: Lippi Marco 
Published: January 2013

AbstractWe shortly reviewed small‐ and medium‐sized standard and dynamic factor analysis, and then concentrate on recent studies on high‐dimensional dynamic factor models. The latter allow the construction of consistent estimators of the factor model, which are based on principal components either in the time or in the frequency domain. Though mainly developed within Time Series Econometrics, high‐dimensional factor models are quite appealing for applications to environmental datasets.
Hotelling'sT2Tests (Robust Versions Of)
Authors: Hallin Marc , Van Aelst Stefan 
Published: January 2013

AbstractHotelling'sT2tests are the classical testing procedure for one‐ and two‐sample location problems under Gaussian assumptions. Although asymptotically valid under non‐Gaussian distributions with finite second‐order moments, they are highly nonrobust. Robust and rank‐based alternatives, resisting violations of the finite second‐order moment assumption, are described.
LAD
Estimation with Applications in Time Series Analysis
Authors: Davis Richard A. , Wu Rongning 
Published: January 2013

Abstract
In this article, we are concerned with least absolute deviation (LAD) estimation applied to linear regression and linear time series models. LAD estimation is widely used in statistical modeling and analysis. Compared to classical least squares (LS), LAD is a robust estimation procedure and is typically more efficient when handling heavy‐tailed data. We provide a selective overview on its developments in linear regression and time series models. This overview includes a description of a basic method for establishing the asymptotic properties of the LAD estimator for finite variance autoregressive moving average (ARMA) models. Efficiency of LAD estimation relative to LS estimation as well as computational issues are also discussed.
Random Fields, Nonparametric Methods
Authors: Tjøstheim Dag 
Published: January 2013

Abstract
An overview is given of nonparametric methods in random fields including estimation of the density, the autocovariance, the specrum, and nonlinear additive models. The difference between expanding domain asymptotics and infill asymptotics is highlighted. Throughout the article similarities and differences relative to time series modeling are pinpointed.
Runs Tests
Authors: Ley Christophe , Paindaveine Davy 
Published: January 2013

AbstractGiven a sequence of two or more types of symbols, a run is defined as a succession of – one or more – identical symbols that are followed and preceded by a different symbol (or by no symbol at all). Runs tests are based on the length of the longest run or on the total number of runs. They figure among the oldest nonparametric procedures, and can be used in various setups. We review the different definitions of runs, their development over the last 100 years, describe the domains of application of runs tests, and discuss their extension to the multivariate case.
Principal Components
Authors: Hallin Marc , Hörmann Siegfried 
Published: January 2013

AbstractPrincipal Components are probably the best known and most widely used of all multivariate analysis techniques. The essential idea consists in performing a linear transformation of the observedk‐dimensional variables in such a way that the new variables are vectors ofkmutually orthogonal (uncorrelated) components – theprincipal components– ranked by decreasing variances. In case the original variables were strongly interrelated, the few first principal components, typically, will account for most of the variation in the original data. Restricting to those few components then allows for a reduction in the dimension of the dataset that retains most of the variability of the original one. Karl. Pearson is generally credited for introducing the method in 1901. The same method was rediscovered in 1933 and popularized by Hotelling; ever since, it has been an essential part of daily statistical practice, basically in all domains of application. From a mathematical point of view, the method is directly rooted in the linear algebra of positive semidefinite matrices and their spectral factorization.
Exponential Families
Authors: Hallin Marc 
Published: January 2013

Abstract
Exponential families of distributions are parametric dominated families in which the logarithm of probability densities take a simple bilinear form (bilinear in the parameter and a statistic). As a consequence of that special form, sampling models in those families admit a finite‐dimensional sufficient statistic irrespective of the sample size, and optimal solutions exist for a number of statistical inference problems: uniformly minimum risk unbiased estimation, uniformly most powerful one‐parameter one‐sided tests, and so on. Most traditional families of distributions–binomial, multinomial, Poisson, negative binomial, normal, gamma, chi‐square, beta, Dirichlet, Wishart, and many others–constitute exponential families. Note, however, that the uniform, logistic, Cauchy, or Student (for given degrees of freedom) location‐scale families are not exponential; the double‐exponential or Laplace family is exponential for scale only, at fixed location.
Microbial Bioremediation of Metals and Radionuclides
Authors: Gadd Geoffrey M. 
Published: January 2013

Abstract

Microorganisms possess a variety of mechanisms that effect changes in metal speciation and mobility. As well as being components of natural elemental cycles for metals, these processes may be exploited in environmental biotechnology and almost all metal‐microbe interactions have been examined for removal, recovery, or detoxification of metal, organometal, metalloid, or metal radionuclide pollutants. Metal mobilization can be achieved by, for example, protonation, chelation, chemical and redox transformations, while immobilization can occur by precipitation or biomineralization, or by sorption, uptake, and intracellular sequestration. Redox reactions can mobilize or immobilize metals depending on the metal species involved. Solubilization may enable metal removal from solids such as soils, sediments, dumps, and industrial and mining wastes. Metal immobilization results in insoluble forms of low bioavailability. Biotechnological applications of microbial systems in bioremediation may provide alternatives or adjuncts to conventional treatment methods for solids, and contaminated effluents and wastewaters. While much research is laboratory‐based, there are many developments to pilot/demonstration scale, with several
in situ
and
ex situ
large‐scale processes in successful operation. Metal removal/transformation processes are also intrinsic components of traditional means of water/sewage treatment as well as reed bed, lagoon, and wetlands technologies.
Waste‐to‐Energy Technologies
Authors: KlemeŠ JiŘí Jaromír , Varbanov Petar Sabev 
Published: January 2013

AbstractThe features and properties of waste as a fuel for converting waste to energy (WTE) are analyzed considering various types of waste. Nonthermal treatments are also covered – for example, anaerobic digestion and microbial fuel cells. Benefits from best available and best applicable techniques (BREFs) developed with the support from the European Union are discussed.
Flow Over Complex Terrain, Numerical Modeling Of
Authors: Lundquist Katherine A. , Katopodes Chow Fotini 
Published: January 2013

AbstractThis article provides a general description of numerical modeling over complex terrain, including both diagnostic and prognostic approaches and their associated advantages and limitations. Diagnostic models solve simplified versions of the equations of motion and are widely used in the engineering community for simulations of flow over complex terrain. These models produce a description of the steady‐state flow, while requiring few inputs and being computationally efficient. Diagnostic models do not predict time evolution of flow, and are often only appropriate for terrain with small slopes. Prognostic models solve the full equations of motion and are used for both engineering and numerical weather prediction applications. Details of the implementation of prognostic models vary widely, but they are capable of producing time‐varying descriptions of the flow and handling very complex terrain with no limitations on terrain slope. These models can require detailed three‐dimensional inputs and large computational resources. Additional simplifications can reduce these requirements, but generally at the expense of functionality, such as producing a steady‐state solution or the inability to handle arbitrarily complex terrain.
Climate Change and North American Great Plains' Drought
Authors: Sauchyn David J. , Bonsal Barrie R. 
Published: January 2013

Abstract
Since human activities and ecosystem health are dependent on adequate, reliable water supplies, droughts pose a serious threat to society and the environment. Over much of the Northern Hemisphere high latitudes, droughts are a recurrent feature of the natural climate as evidenced from direct measurements during the instrumental period, and as inferred from paleo‐reconstructions dating back several centuries. However, concern has been expressed regarding climate‐change impacts on future drought frequency, duration, and severity over various regions of the world and in particular, continental interior regions at high latitudes. This article synthesizes relevant scientific research regarding drought in the North American Great Plains, and the Canadian Prairies in particular. First, we review existing knowledge regarding the large‐scale atmospheric causes of North American drought. This is followed by a synopsis of past trends and variability of drought occurrence in the instrumental and paleo record. We then summarize research on the future drought in a changing climate. This article concludes with the identification of major research gaps that will aid in the ability to understand and predict future changes to Great Plains' droughts.
Nonparametric Curve Estimator
Authors: Tarter Michael E. 
Published: January 2013

AbstractThe roles that nonparametric curve estimators can play are illustrated in studies of polluting chemicals that have built up in the environment and of crop forecasting techniques. The multivariate normal model and model‐free curve estimator representations are compared. Equations and approaches are described that enable researchers to input prior information that divides variates into two categories, variates of primary importance and variates that are nuisances in the sense that their detained distributional structure is relevant solely insofar as its effects on variates of primary importance. It is shown that conditional density estimators can be computed given user‐selected values of nuisance variates, not computed as a second step that follows a first step, which requires the estimation of a high‐dimensional joint distribution. It is argued that this approach resembles parametric methodology that has long been fruitfully employed in the field of experimental design.
Nanoparticles: Environmental Effects
Authors: Pettitt Michala E. , Römer Isabella , Lead Jamie R. 
Published: January 2013

Abstract

Manufactured nanoparticles (NPs) possess novel physicochemical properties which may have unforeseen environmental effects. Accurately describing the ecotoxicity of NPs requires a more complete understanding of their properties and behavior
in vitro
. The metric chosen as the descriptor of dose can profoundly influence the interpretation of the dose‐response curve and derived ecotoxicity endpoints. Accurately determining the actual dose (or exposure) received by a test organism during ecotoxicity assays requires appropriate characterization of particles
in situ
in exposure media. Characterization should be conducted before and after exposure, as a minimum. Silver nanoparticles (AgNPs) are the most common nanomaterial currently in commercial production and are predicted to have pervasive release to aquatic environments. Mechanisms of action for AgNPs are discussed and cytotoxic, genotoxic and whole‐organism endpoints are reviewed.
Climate Change Scenarios for Impacts Assessment
Authors: Zwiers Francis W. , Bürger Gerd 
Published: January 2013

Abstract
The study of the impacts of potential future climate change and evaluation of actions that might be used to reduce those impacts requires the development of plausible scenarios of future climate change and variability. Projections of future climate change are produced by climate modeling centers using physically based climate system models that are driven with scenarios of future greenhouse gas emissions. While climate models have been increasing in complexity and resolution, the climate projections that they produce generally still require postprocessing to correct biases and to further refine resolution so that the output can be used to inform local and regional impacts assessments, and to drive impacts models, such as crop models or fine‐scale surface hydrology models. This article briefly describes several of the “statistical downscaling” techniques that are used for this postprocessing.