bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust Quantile Regression
Authors: JureČková Jana 
Published: September 2006

Abstract

Robust quantile regression is a collection of statistical procedures based on
regression, autoregression and conditional quantiles
and on
regression and autoregression rank scores
. These procedures can replace the classical estimation and testing procedures in the linear regression and autoregression models, based on least squares, in situations when we cannot guarantee that our observations follow the normal distribution. The character of these methods is nonparametric (i.e. they do not assume any specific probability distribution of the observations), and robust (with respect to outliers in the dependent variables). Moreover, the procedures based on the regression rank scores do not depend on an eventual nuisance regression, because the regression rank scores are invariant to the regression parameters, similarly as the ordinary ranks are invariant to the shift in location. Regression quantiles and regression rank scores are dual in the linear programming sense, but their relation also extends the duality of the quantiles (order statistics) and ranks from the location to the linear regression (autoregression) models.
Sample Support
Authors: Gotway Crawford Carol A. 
Published: September 2006

Abstract

Sample support generally refers to the length, area, or volume associated with a measurement or observation. The term originates in the field of
geostatistics
, primarily from mining applications. To appreciate the issues associated with this term, this article provides: 1. a more complete definition of sample support as used in geostatistics; 2. a description of the problems that arise in working with data of differing supports; 3. a brief overview of the solutions to change of support problems.
Sediment Transport
Authors: James Alec E. 
Published: September 2006

Abstract
Sediment transport occurs when the flow of water over a surface is rapid enough to erode and move surfacial sediments; if the current decreases then deposition of all or some of the sediments will occur. In practice the process is extremely complex and once movement is initiated the properties of the fluid are modified by the presence of the irregularly shaped solid particles from which the bed‐form is comprised. The range of particle size is large and this in itself leads to different ways of describing sediment transport phenomena. There are two fundamental modes of transport, the first in which particles roll or slide along the bed of a river or the sea and the second in which the particles are entrained by the flow and move in suspension with the water mass. Evidently the amount of material transported will depend on factors such as the particle size and shape; the density will be important, as will the local intensity of turbulence and the magnitude of the average advective velocity of the water together with its own density.
Risk Assessment, Seismological
Authors: Brillinger David R. 
Published: September 2006

AbstractSeismic risk assessment(SRA) may be defined as the process of estimating the probability that certain performance variates at a location of interest exceed relevant critical values, within a specified time period, as a result of nearby seismic events. Possible performance variates include: maximum acceleration, force (on an object), amplitude of vibration at a specified frequency, damage.
Smoothing
Authors: Kafadar Karen , Horn Paul S. 
Published: September 2006

AbstractWe describe smoothing as an exploratory tool in data analysis, including an underlying model that motivates the need for smoothing, the goals and characteristics of good smoothers, and general classes of smoothers (linear and nonlinear). We also relate smoothing to the concepts of general fitting, parametric and nonparametric regression, and illustrate the use of smoothers on both one‐ and two‐dimensional data sets.
Space Debris Risk
Authors: Brillinger David R. 
Published: September 2006

AbstractSpace debrishas been defined as manmade objects or parts thereof in space that do not serve any useful purpose. There is now a very substantial amount of such ‘space junk’ circling the earth and posing a risk to active and inactive satellites, space stations and spacecraft. This debris presents a problem, with substantial environmental and legal implications. It is yet another pollution problem. This debris has resulted from the more than 3000 space flights that have taken place since 1957. The general concern is risk assessment, that is: estimating the probability of some future event of adverse consequence. In the present circumstance space debris risk assessment might be defined as: The estimation of the probability that certain performance variates of a space object of interest exceed relevant critical levels, within a specified time period, as a result of collisions with space debris.
Spacing Trials
Authors: Burkhart Harold E. 
Published: September 2006

Abstract
One of the most important factors that is within the direct control of managers of plant crops is plant spacing. Managers of agronomic, horticultural, and forestry cropping systems must decide on an initial planting density. Economic considerations dictate that one strives for the ‘optimal’ spacing of plants. The optimum spacing will, of course, vary depending on the management objective. Designed spacing experiments provide reliable information on optimal density for given product objectives.
Spatial Covariance
Authors: Sampson Paul D. 
Published: September 2006

Abstract
Modeling of the spatial dependence structure of environmental processes is fundamental to almost all statistical analyses of data that are sampled spatially. Such analyses address tasks such as spatial estimation (Kriging), monitoring network design (Optimal spatial design), as well as the basic scientific characterization of the second order properties of these processes. This article reviews the current literature under the headings of: smoothing and kernel methods, basis function models, process convolution models, spatial deformation models, and concludes with brief mention of parametric models and further discussion.
Spatial Distribution
Authors: Waller Lance A. 
Published: September 2006

AbstractModeling of the spatial dependence structure of environmental processes is fundamental to almost all statistical analyses of data that are sampled spatially. Such analyses address tasks such as spatial estimation (Kriging), monitoring network design (Optimal spatial design), as well as the basic scientific characterization of the second order properties of these processes. This article reviews the current literature under the headings of: smoothing and kernel methods, basis function models, process convolution models, spatial deformation models, and concludes with brief mention of parametric models and further discussion.
Spatial Optimization
Authors: Hof John 
Published: September 2006

AbstractThis article describes an emerging methodology that captures spatial relationships between different land areas in the process of maximizing an objective function subject to resource constraints. It discusses adjacency constraints and direct approaches. Adjacency constraints are utilized when scheduling timber harvests with legal or regulatory restrictions on the effective size of harvest area. Adjacency relationships are important because, for example, if two seemingly legal‐size clearcuts occur within a short time of each other in adjacent areas, then the effective size of the clearcut may not be legal. Direct approaches include the actual ecological spatial relationships of concern. An example is provided that accounts for wildlife habitat fragmentation in optimally placing timber harvests. The land area is divided into cells and choice variables are defined to represent complete scheduled management prescriptions for each cell. Wildlife population levels are then determined by a combination of potential growth, dispersal, and spatially located carrying capacities determined by the management prescription allocations. Other examples of this type of direct spatial optimization are also cited.
Spectrum Analysis
Authors: Zurbenko Igor G. , Porter P. Steven 
Published: September 2006

AbstractTime series of environmental quality measurements often display periodic or quasi‐periodic behavior on a wide range of time scales. A clearer understanding of the forces behind these processes may be available through spectral analysis, a transformation of variation in time into variation as a function of frequency or period. This article is intended to introduce an applied scientist to spectral analysis. Spectral analysis of stationary time series is briefly described, and avenues for the exploration of nonstationary time series, which represent the vast majority of time series encountered in practice, are suggested.
S‐PLUS
Authors: Storvik Geir 
Published: September 2006

Abstract
S‐PLUS is an integrated suite of software facilities for data analysis and graphical display. It is an enhanced, supported superset of the S language developed at AT&T Bell Laboratories. It contains standard statistical tools such as linear regression and smoothing, analysis of variance, generalized linear models, multivariate analysis, time series analysis, survival analysis and quality control charts. In addition, many complex, modern techniques are included. Further, it offers a language for expressing statistical models and tools for using linear, generalized linear and nonlinear statistical models.
Standard Error
Authors: Hughes‐oliver J.M. 
Published: September 2006

Abstract

The standard error (se) of a sample statistic is a measure of the variability of the statistic around its ‘typical’ value. The precise definition of se is, however, not universally agreed upon. Suppose

T
X

,
n

≡
T
(
X
1
, …,
X
n
) is the statistic of interest calculated from the sample
X
1
,…,
X
n
. The se of the statistic
T
is sometimes defined as the standard deviation (sd) of the distribution of
T
and at other times as the estimated sd of the distribution of
T
. The latter definition is also often referred to as the estimated se. Understanding se clearly requires understanding sd.
Standards, Environmental
Authors: Barnett Vic , Bown Marion 
Published: September 2006

Abstract
This article aims to describe the past, present and future techniques by which environmental standards are set. Recent concern regarding the lack of acknowledgment of uncertainty and variability in many standards, and thus in reported results on compliance, has lead to an emphasis on the role of statistical methodology in environmental standard‐setting. Research into the feasibility, advantages and disadvantages of such methodology is on going, but much remains to be done. Encompassing within its line of investigation toxicology, environmental sampling methodology and spatio‐temporal modeling, etc. and affecting the fundamental procedures of industry, traffic management, medical and environmental agencies, etc., to name just a few of many, this area of the rapidly growing field of environmetrics may prove to be one of the most far‐reaching and important statistical applications in the coming years.
State‐Space Methods
Authors: Koopman S.J. 
Published: September 2006

Abstract
The state space model provides an unifying framework for analyzing time series. For the classical Box–Jenkins analysis, state space methods are useful when estimating the autoregressive and moving average coefficients by exact maximum likelihood and when missing values are present. In the case of unobserved components time series models with interpretable components such as trend, seasonal, cycle and irregular, state space methods provide the tools for estimation, signal extraction and diagnostic checking. Indeed, all linear time series models can be cast into state space including dynamic regression models and cubic spline smoothing models.
Statistical Computing in Environmental Science
Authors: Stein Alfred 
Published: September 2006

Abstract

Environmental modeling can be distinguished into
modeling
the environment or some of its compartments,
predicting
environmental parameters, and
storing
and
monitoring
data on the quality of the environment. Computational aspects of environmental science concern data storage, modeling of processes, modeling of effects, graphical display of spatial data and scenario analysis. Computers and computing enter environmental sciences actively and outside research when decision making has to be supported. This requires reliable spatial data, reliable models and interactive tools for visualizing scenarios.
Modeling, Stochastic
Authors: Christakos George 
Published: September 2006

Abstract
Perceptions regarding the meaning of the term stochastic modeling (SM) are not as uniform as one might think. This should not come as a surprise, in view of the multidisciplinary nature of environmental health science. Depending on the application considered, one may refer to stochastic models as environmental, ecologic, genetic, epidemiologic, medical, etc. A common factor in all these cases is that SM is concerned with the mathematically rigorous and scientifically meaningful representation, explanation, and prediction of natural phenomena (physical, biological, etc.) in uncertain environments. Uncertainty may be due to measurement errors, heterogeneous databases, erratic biologic fluctuations in space/time, insufficient knowledge, etc. Within such a framework, SM seeks to provide a realistic environmental situation with spatio‐temporal continuity and internal consistency.
Subset Selection
Authors: Van Der Laan Paul 
Published: September 2006

AbstractAssumek(k ≥ 2) populations are given. The associated independent random variables have continuous distribution functions differing only in their unknown location parameter. The statistical selection goal of subset selection is to select from the given set ofkpopulations a non‐empty subset of these populations so that the selected subset includes with confidence levelP*, withk−1 < P* < 1, the best population. In this context, best population means the population with the largest value of the location parameter. The subset selection approach is described in some detail and some properties are presented.
Surveillance, Environmental
Authors: Järpe Eric 
Published: September 2006

Abstract
During most of the previous century, there was a great need for surveillance (also called quality control) methods from the manufacturing industry. Through the last few decades, however, the picture has changed. The demands for surveillance methods from other applications have increased. In order to take appropriate actions to prevent pollution and other environmental deterioration, fast and accurate indications about when a systematic change in an environmental system occurs is crucial. The subject‐matter application determines which models are appropriate, and what properties of surveillance methods that are especially important. For this purpose it is necessary to monitor relevant environmental processes for treatment in time and space.
Switching Process
Authors: Shumway Robert H. 
Published: September 2006

Abstract

Often, there is a need to develop models that describe environmental processes that change regimes at unknown points in time or space. Dynamic regression or state–space models that are switching from one regime to the other according to an evolving stationary discrete state Markov chain have great potential for modeling changing environmental phenomena. Processes that involve these kinds of models are often referred to as
switching processes
or hidden Markov processes.
Teratogenesis, Environmental
Authors: Coull Brent A. , Holmes Lewis B. 
Published: September 2006

Abstract
Teratogenesis is defined as the induction of malformations or defects in fetuses during gestation or at birth. As one might expect, induction of birth defects is a major public health concern. Many of these birth defects are attributed to the process of multifactorial inheritance in which both genetic and nongenetic factors are presumed to be involved. Unfortunately, the large majority of the nongenetic factors remain unidentified.
Trend Analysis
Authors: Harvey Andrew 
Published: September 2006

AbstractThe question of defining a trend is one which has exercised the minds of statisticians for many years. In much of the statistical literature, a trend is conceived of as that part of a series which changes relatively slowly over time. Viewed in terms of prediction, the estimated trend is that part of the series which when extrapolated gives the clearest indication of the future long‐term movements in the series. Trend analysis is best carried out by means of unobserved‐component, or structural, time‐series models. The trend components in structural time‐series models are always set up in such a way that their forecast functions indicate the long‐term movements in the series, and the trend within the series can be obtained by signal extraction methods.
Trimming and Winsorization
Authors: Horn Paul S. , Kafadar Karen 
Published: September 2006

Abstract
Frequently, a sample may contain observations that differ substantially from the other data values. There are various ways to deal with the possibility of such values so that the resulting analysis will not be adversely affected if they are present. All of these methods assign weights to observations according to some well‐conceived algorithm. In this article we concentrate on two forms of assigning weights to observations. The first is trimming, which corresponds to assigning zero weight to a prespecified fraction of the extreme observations and equal weights to the rest. The second is Winsorizing, which also assign zero weights to the extreme observations, as in trimming, but assigns extra weight to the most extreme observations in the retained sample. In this article we will define these concepts for a single sample of observations, and briefly discuss the applications to more complex problems like regression.
Weighted Distributions
Authors: Patil G.P. 
Published: September 2006

Abstract
Traditional environmetric theory and practice have been occupied largely with randomization and replication. But in environmental and ecological work, observations also fall in the nonexperimental, nonreplicated, and nonrandom categories. The problems of model specification and data interpretation then acquire special importance and great concern. The theory of weighted distributions provides a unifying approach for these problems. Weighted distributions take into account the method of ascertainment, by adjusting the probabilities of actual occurrence of events to arrive at a specification of the probabilities of those events as observed and recorded. Failure to make such adjustments can lead to incorrect conclusions.
Zero‐Inflated Data
Authors: Tu Wanzhu 
Published: September 2006

Abstract

As its name suggests, zero inflation indicates that a dataset contains an excessive number of zeros. The word
inflation
is used to emphasize that the probability mass at the point zero exceeds that allowed under a standard parametric family of distributions. Zero‐inflated data are abundant in many disciplines, including the environmental sciences. If not properly modeled, the presence of excess zeros can invalidate the distributional assumptions of the analysis, jeopardizing the integrity of the scientific inferences. The zeros can also create many computational difficulties. Recently, a fair amount of interest has concentrated on the analysis of zero‐inflated Poisson (ZIP) counts. Strictly speaking, however, the data need not follow a Poisson distribution in order to be zero inflated. In this article, a broader definition of the subject is taken; zero inflation is considered as a practical data phenomenon that can be associated with any parametric family of distributions.
Analysis of the 5HT‐2A T102C receptor polymorphism and psychotic symptoms in Alzheimer's disease
Authors: Craig David , Donnelly Caroline , Hart Dominic , Carson Robyn , Passmore Peter 
Published: September 2006

AbstractAlthough the aetiology of psychotic symptoms in Alzheimer's disease (AD) is multi‐factorial, alterations in serotonergic neurotransmission are often implicated. Polymorphisms of the serotonin receptor 5HT‐2A are associated with hallucinatory symptoms and delusions in demented and non‐demented cohorts. This study examined the role of the 5HT‐2A T102C polymorphism in influencing psychotic symptoms in a large Northern Ireland AD population (n = 406, mean MMSE 13/30). Forty‐eight percent of patients experienced delusional symptoms and 28% experienced hallucinations during the course of their dementia. No significant association was found either in frequency of genotype or allelic variation for either set of symptoms. Furthermore, the mean delusional and hallucinatory severity scores did not differ significantly among the three genotype groups. The lack of influence of the T102C polymorphism of the 5HT‐2A receptor on the emergence of psychotic symptoms in AD contrasts with previous reports in other cohorts involving smaller numbers of subjects. © 2006 Wiley‐Liss, Inc.
Simulation of an all‐β3‐icosapeptide containing the 20 proteinogenic side chains: Effect of temperature, pH, counterions, solvent, and force field on helix stability
Authors: Trzesniak Daniel , Jaun Bernhard , Mathad Raveendra I. , van Gunsteren Wilfred F. 
Published: September 2006

AbstractSimulations of various β‐peptides have in the last years clarified several issues concerning peptide folding equilibria and interpretation of experimental data, especially from NMR and CD spectroscopy. These simulations involved different temperatures, pH‐values, ionic strengths, solvents, and force‐field parameters, but a variation of these factors for one β‐peptide has not yet been done. To investigate the influence of varying these factors, we analyze the helix stability of an all‐β3‐icosapeptide bearing all 20 proteinogenic amino acid side chains, which is experimentally observed to fold into a 314‐helix in methanol but not in water. Structural aspects, such as hydrogen‐bonded rings and salt bridges, are discussed and a comparison with NMR primary (NOE distance bounds and 3J‐values) and secondary (NMR derived model structures) data is made. We further investigate the reasons for the 314‐helix stability/instability in methanol/water. Of all factors studied, the presence of counterions seems to be the one inducing most significant effects in the simulations. © 2006 Wiley Periodicals, Inc. Biopolymers 83: 636–645, 2006This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
Meta-analysis of endoscopy and surgery versus surgery alone for common bile duct stones with the gallbladder in situ
Authors: Clayton E S J , Connor S , Alexakis N , Leandros E 
Published: September 2006

Abstract

Background
There is no clear consensus on the better therapeutic approach (endoscopic versus surgical) to choledocholithiasis. This study is a meta-analysis of the available evidence.

Methods
A search of the Medline and ISI databases identified 12 studies that met the inclusion criteria for data extraction. The analysis was performed using a random-effects model. The outcome was calculated as an odds ratio (OR) or relative risk (RR) with 95 per cent confidence intervals (c.i.).

Results
Outcomes of 1357 patients were studied. There was no significant difference in successful duct clearance (OR 0·85 (95 per cent c.i. 0·64 to 1·12); P = 0·250), mortality (RR 1·79 (95 per cent c.i. 0·66 to 4·83); P = 0·250), total morbidity (RR 0·89 (95 per cent 0·71 c.i. to 1·13); P = 0·350), major morbidity (RR 1·34 (95 per cent c.i. 0·92 to 1·97); P = 0·130) or need for additional procedures (OR 1·37 (95 per cent c.i. 0·82 to 2·29); P = 0·230) between the endoscopic and surgical groups. There was also no significant difference between the endoscopic and laparoscopic surgery groups.

Conclusion
Both approaches have similar outcomes, and treatment should be determined by local resources and expertise.