bluuebunny/crossref_metadata_2025_split huggingface dataset:
Some advances in non‐parametric multiple time series and spectral analysis
Authors: Walden A. T. 
Published: February 2007

AbstractMultiple time series usually arise in measurements on physical systems in one of two ways. The first way is when a set of time series arise on an “equal footing.” A distant explosion might be recorded at several contiguous recording sites. From such series can be determined, for example, characteristics of the signal and noise content of the data. The second way is when the series are causally related. Northward and eastward wind velocity series at a coastal location might be inputs to a linear system, which with additive noise produces on output the northward wind velocity at a buoy location. Here we might wish to estimate some properties of the linear system.Many physical systems exhibit a large dynamic range. We show how the technique of multitaper spectral analysis can be used to much improve the analysis of series which arise on an ‘equal footing.’ This is illustrated using multiple time series recordings of seismic explosions. We show how multitaper spectral analysis (a) can better detect the presence or absence of 60 Hz “power‐line pick‐up” for dynamite data recorded in Canada, and (b) more accurately characterize seismic exploration data in terms of coherence and signal‐to‐noise ratios.For the case of two causally related time series with additive non‐white noise we show how it is possible to calculate a confidence interval for the mean square error of the signal component. This is an advance on traditional methods which merely detect the presence or absence of the signal. The approach is demonstrated using some seismic data recorded at sea.
A new approach to the identification of model structure
Authors: Stigter J. D. , Beck M. B. 
Published: February 2007

AbstractMost models of environmental systems are based on sets of differential equations. The paper investigates the problem of identifying the number and form of appropriately parameterized terms in such continuous‐time state‐space models, a problem referred to as model structure identification. Filtering theory (recursive estimation) is used as an approach to the solution of this problem. Central to this approach is the notion that the patterns of the (posterior) trajectories of the model's parameters, when contrasted with the prior assumptions about their expected variability, will yield insights into the adequacy, or otherwise, of a candidate model's structure. The particular algorithm employed herein is based upon an analysis of Ljung (1979), who proposed a significant modification of the conventional extended Kalman filter wherein the elements of the Kalman gain matrix may be estimated directly as unknown parameters of an innovations process representation of the system's behaviour. Whereas Ljung's filter was designed for an entirely discrete‐time system, the present version of the filter has been derived for a system with continuous‐time dynamics and discretetime observations. Using time series data from the River Cam, the paper presents a case study in identifying a sequence of three candidate model structures for describing the assimilation and generation of easily degradable organic matter. The trajectories of recursive estimates for the elements of the gain matrix provide informative insights into, and better defined evidence of, the failure of an inadequate model structure.
Transformation to symmetry for censored data caused by a detection limit
Authors: Nakamura Miguel , Díaz‐Francés Eloísa 
Published: February 2007

AbstractTransformations to symmetry in the Box–Cox family for left‐censored observations having a common lower detection limit are considered. Recently, transformations to normality in this setting have been discussed. Symmetry, rather than normality, may be a more realistic assumption, for instance, if an estimated transformation to normality displays heavier tails than those presumed under a normal model. A method for selecting a transformation to symmetry, based on the sample skewness coefficient, is described. The advantages of transformation using this method are illustrated in a simulation study, assuming that the goal in transformation methodology is to increase precision in the estimation of the mean, quantiles, and the distribution function of the variable in its original scale. An example regarding censored measurements in polluted water samples is provided.
Combining wilcoxon tests with censored data: An application to well water contamination
Authors: Korn Leo R. , Murphy Eileen A. , Zhang Zhiyi 
Published: February 2007

AbstractA sampled data set can often be classified into several groups, say K, defined by covariates. While within each group it is usually reasonable to assume that the observations are homogeneous, group effects are frequently far from being negligible. Suppose it is of interest to compare two treatments non‐parametrically. A rank test is naturally called for within each group. However, the group effects, in location as well as in scale, prevent the rank test from being carried out with pooled data across the groups. A suitable way to compare treatments is to combine the K independent rank tests, one from each group. The key to such a combining procedure is a good weighting scheme which typically is a function of the sample sizes and sample dispersion. In order to use the combined statistic for testing, the weights need to be consistent so that inferences can be made based on the asymptotic null distribution of the combined test statistic. However, when there are censored observations, non‐parametric consistent estimators of dispersion parameters are usually nonexistent because of the lack of tail information. Zhang offered a method of accommodating the consistency problem through estimating the ratios of group dispersion parameters. The consistency of the weights are supported by the consistency of the quantile estimation based on Kaplan–Meier estimators. The purpose of this paper is twofold. First, of statistical interest, it is to illustrate the application of the general method proposed by Zhang with some well water contamination data. Second, of environmental interest, it is shown that application of fertilizer increases the level of nitrate contamination in water from shallow wells.
An empirical bayes approach to monitoring water quality
Authors: Solow Andrew R. , Gaines Arthur G. 
Published: February 2007

AbstractThe level of faecal coliform bacteria in a water body is commonly monitored by multiple tube fermentation experiments. In many cases, interest centres on detecting the exceedance of a standard. This paper describes an empirical Bayes approach to estimating standard exceedance probability from multiple tube fermentation experiments. An example is given.
The aral sea desiccation and possible ways of rehabilitating and conserving its northern part
Authors: Aladin N. V. , Plotnikov I. S. , Potts W. T. W. 
Published: February 2007

AbstractThe changes in the various components of the water balance of the Aral Sea, since 1960, and the consequent effects on salinity, surface area and water levels, are described. Since 1989 the fall in level has divided the Aral Sea into two parts, a northern Small Aral Sea and a southern Large Sea. The Small Sea receives part of the inflow of the Syrdar'ya and since 1990 its level has risen and its salinity has declined. To prevent the Small Sea overflowing into the south, a dam was constructed. Although this soon failed, the fauna and flora has begun to recover to some extent in the north Aral Sea. The desirability of a scientific and engineering programme to rehabilitate the northern sea is discussed.
Time modelling and spatial clustering of daily ambient temperature: An application in Southern Italy
Authors: Macchiato M. F. , La Rotonda L. , Lapenna V. , Ragosta M. 
Published: February 2007

AbstractKnowledge of the spatial and temporal patterns of meteoclimatic variables is one of the crucial points when dealing with environmental problems. For this purpose we analysed the temporal and spatial behaviour of ambient daily temperature values in order to single out the time dynamics of this variable and its correlation with landscape parameters. A method based on two techniques is implemented; the first describes the time dynamics using stochastic models and the second characterizes the homogeneous areas using a multivariate algorithm for spatial clustering. This approach allows us to use a powerful and parsimonious description of temporal and spatial patterns of daily ambient temperatures. Our analysis relates to temperatures but the approach is completely general and can be applied to other meteoclimatic variables. We checked the proposed method in the study of daily ambient temperature patterns in Southern Italy. The data, collected during the period from 1973 to 1988 in 50 thermometric stations located in two regions of Southern Italy (Basilicata and Calabria), are analysed.
Two‐stage sampling for bounding a mean
Authors: Atwood Corwin L. , Bryan Mark F. 
Published: February 2007

AbstractConsider comparing the mean of some quantity to a fixed allowed maximum using an upper confidence limit. If the sample mean of the measurements is below the allowed maximum, but the confidence limit is above it, a widespread practice is to increase the sample size and recalculate the confidence bound. The confidence level of this two‐stage procedure is rarely found correctly, but instead is typically taken to be the nominal confidence level, found as if the final sample size had been specified in advance. In typical settings the correct nominal α should be between the desired P(type I error) and half that value. This paper gives tables for the correct α to use with selected sample sizes, some plots of power curves, and an example of correct two‐stage sampling.
Non‐linear mixed regression models
Authors: Burnett Richard T. , Ross W. H. , Krewski Daniel 
Published: February 2007

AbstractIn this paper we present an estimating equation approach to statistical inference for non‐linear random effects regression models for correlated data. With this approach, the distribution of the observations and the random effects need not be specified; only their expectation and covariance structure are required. The variance of the data given the random effects may depend on the conditional expectation. An approximation to the conditional expectation about the fitted value of the random effects is used to obtain closed form expressions for the unconditional mean and covariance of the data. The proposed methods are illustrated using data from a mouse skin painting experiment.
Statistical analysis of asbestos fibre counts
Authors: Oehlert Gary W. , Lee Richard J. , Van Orden Drew 
Published: February 2007

AbstractWe present methods for estimating asbestos fibre concentrations based on the negative binomial distribution for fibre counts. This distribution is preferred to the usual Poisson due to the extra variability encountered in the data. We extend the method to include estimation of the parameters of a log‐normal population of concentrations, and deal with the problem of zero counts. We illustrate the methods on a set of 921 fibre counts from 177 school buildings.
Spatial modelling of snow water equivalent using airborne and ground‐based snow data
Authors: Carroll Steven S. , Day Gerald N. , Cressie Noel , Carroll Thomas R. 
Published: February 2007

AbstractAs the population and industrial infrastructure of the United States continue to grow, the demand for water and the need to forecast water resources accurately are intensifying. Hence, the National Weather Service maintains a set of conceptual, continuous, hydrologic simulation models used to generate extended streamflow predictions, water supply outlooks and flood forecasts that are the basis for major water management and disaster emergency services decisions for the United States. A vital component of the hydrologic simulation models is a snow accumulation and ablation model that uses observed temperature and precipitation data to simulate snow cover conditions. The simulated model states are updated throughout the snow season using snow water equivalent estimates obtained from airborne and ground‐based snow water equivalent data. The National Weather Service has developed a spatial model to obtain integrated snow water equivalent estimates for updating the snow model; however, it is designed to incorporate only ground‐based data. In this research, we describe the spatial model and show how to modify it to include data of two different supports (the airborne and ground‐based data) so that more precise integrated snow water equivalent estimates can be obtained. The results are illustrated on snow data collected in the Upper Colorado River basin.
Metabolic responses in the assessment of pollution effects
Authors: Pires Ana , Branco Joao , Picado Ana , Mendonça Elsa 
Published: February 2007

AbstractAdenylate nucleotide concentrations in the polychaete Lanice conchilega were measured and the index adenylate energy charge (AEC) was calculated at different times of the year. The organisms were sampled at three sites in the Sado River estuary, two of them located near the outfall of a pulp mill effluent and a third one in the south bank, considered as reference. A statistical analysis was conducted on the resulting observations in order to evaluate the site influence. Significant differences were found on the AEC values between more and less polluted sites. Another index was built up using principal components after adequately transforming the initial variables. A covariance model with the variable time as covariate was fitted, using GLIM, to see how this index can be used to distinguish between sites. Finally, robust procedures were applied in order to obtain more robust fits.
A non‐linear model of an open flow adsorption process
Authors: Argyelan J. , Nassar R. 
Published: February 2007

AbstractA probabilistic model is developed that describes the dynamics of adsorption in open flow reactors in the case of non‐linear adsorption kinetics. The model accounts for the movement of solute molecules in the liquid phase, for the exchange of solute molecules between the liquid and outer layer (or compartment) of the solid phase and for movement of solute molecules between the outer and inner layers of the solid phase. From this model, one can predict the concentration of solute molecules in the exit stream of the reactor (breakthrough curve) over time. The model was applied to breakthrough curve data for trichloroethylene adsorption from water onto activated carbon. Model prediction was in good agreement with experimental data.
An application of locally weighted regression to airborne mercury deposition around an incinerator site
Authors: Opsomer Jean‐Didier , Agras Jean , Carpi Anthony , Rodrigues Geraldo 
Published: February 2007

AbstractIn this paper the deposition of mercury around an incinerator site is modelled using a non‐parametric technique, locally weighted least squares regression. We begin by summarizing current theory on how multivariate locally weighted linear regression can be used to generate a regression surface and to construct approximate F‐tests. We detail a simple implementation of the method that does not involve an adaptive bandwidth. This method is then applied to data on mercury accumulation in moss samples around an incinerator site, a situation where parametric modelling is not appropriate because of the small sample size and the presence of many potential covariates. The predicted regression surfaces are displayed using different bandwidth choices and suggest that the incinerator indeed affects the spatial distribution of mercury in the immediate vicinity of the incinerator. Approximate F‐tests indicate that this effect is statistically significant at the 10 per cent level only after the moss samples have been oven‐dried. A potential explanation for this finding based on the relative volatility of different mercury species is discussed.
Aluminium and the risk for alzheimer's disease
Authors: McLachlan D. R. C. 
Published: February 2007

AbstractThe strength of the evidence implicating aluminium as an important factor in the dementia of Alzheimer's disease (AD) is reviewed. We submit that the weight of the epidemiological and biological arguments is considerable. While a complete understanding has not been achieved, we recommend lowering aluminium exposure in municipal drinking water to below 50 μg/l in at least the province of Ontario. The Ministry of Energy and the Environment of Ontario maintains a drinking water surveillance programme which has provided a database for more than 10 years. This database has permitted assessment of the relative risk for AD using four independent measures of disease prevalence: 1. weighted residential history as a measure of exposure in autopsy‐verified AD cases and controls; 2. first admission hospital discharge diagnoses of AD and controls; 3. death certificate rates for AD and presenile dementia; 4. impairments in cognitive function in elderly males. Taken together, many of the criticisms applied to epidemiological studies conducted elsewhere and reaching similar conclusions have been met. A vast experimental database on aluminium neurotoxicity, rapid advances in the understanding of the molecular basis of AD, and comparison studies between human brains exposed to chronic low dose aluminium exposure secondary to renal failure and AD, lend strong biological support to the conclusions reached by the epidemiological studies. The factors initiating AD, how aluminium gains access to the brain in AD, and the relative contributions of food, pharmaceuticals and skin absorption, remain unknown. While a full understanding is not in hand, the devastating nature of the disease, the lack of an effective treatment or prevention and the high cost to the health care system, together with the human costs, weighed against the relative cost of moderately reducing drinking water aluminium concentration to reduce exposure indicate that action is both reasonable and timely.
Environmental tobacco smoke and lung cancer: Uncertainties in the population estimates but not in the causal association — a rejoinder to gross
Authors: Bayard Steven , Jinot Jennifer , Flatman George 
Published: February 2007

AbstractThis article is a rejoinder to the article by Gross in this issue, in which he claims that ‘any range [of estimates of lung cancer deaths attributable to environmental tobacco smoke (ETS)] must encompass an estimate of zero deaths, and that the uncertainties are so great as to cast serious doubt on the use of any quantitative estimate’.The U.S. Environmental Protection Agency's (EPA) conclusion that ETS is a human lung carcinogen was based on the total weight of evidence, not just the 11 U.S. epidemiology studies of ETS and lung cancer that Gross discusses with respect to the population risk estimates. This conclusion was unanimously endorsed by the Agency's independent Scientific Advisory Board and is in concurrence with the conclusions of other organizations.As for the population risk estimates, the EPA report acknowledges uncertainties in these estimates. However, we maintain that the estimate of 3000 lung cancer deaths per year in U.S. non‐smokers is a reasonable point estimate. Furthermore, we have relatively high confidence in this estimate compared to cancer risk estimates for other environmental pollutants, because it is based on human data from actual environmental exposure levels.
A new model pdf for contaminants dispersing in the atmosphere
Authors: Lewis D. M. , Chatwin P. C. 
Published: February 2007

AbstractThis paper illustrates the modelling of the probability density function for the concentration of a scalar dispersing in the atmosphere, using a simple modification of a two state distribution relevant to the hypothetical case of no molecular diffusion. The low concentration values are modelled by means of an exponential distribution, and the high concentration tails by a generalized Pareto distribution. The fits are generally better than those obtained using the beta distribution considered in earlier work. The new model links well with the α‐β theory for concentration moments in turbulent flows, discussed in other papers of this special issue ofEnvironmetrics.
A prognosis for the sudden release of contaminant in an environmental flow
Authors: Sullivan P. J. , Ye H. 
Published: February 2007

AbstractA diffusing contaminant cloud is characterized by its location, size and state. The state of contaminant concentration reduction is the main concern in this paper and is represented by the expected‐mass‐fraction function. A bi‐Gaussian probability density function for concentration is developed and shown to describe measured probability density functions throughout the bulk of a variety of steady flows with continuous contaminant sources. The bi‐Gaussian density function is shown to be directly related to the α‐β prescription of moments and to enable the approximate prediction of the expected‐mass‐fraction function. A puff of contaminant released in the neutral atmospheric boundary layer is used as an illustrative example.
An outline of ecometric analysis to establish load diagrams for nutrients/eutrophication
Authors: Håkanson Lars , Wallin Mats 
Published: February 2007

AbstractAn outline of some central methodological steps in an ecometric analysis to develop load diagrams is given. This is done within the framework of an Expert system for environmental consequence analysis for natural aquatic ecosystems. Examples focus on nutrient emissions/eutrophication in coastal areas. The analysis is based on a number of fundamental concepts (effect, dose, sensitivity, area and time compatibility, ecometric analysis, load diagrams, etc.). The paper addresses questions concerning the basic set‐up of the Expert system, how to establish representative empirical data for entire coastal areas for various types of substances, e.g., nutrients, and parameters expressing eutrophication (e.g., Secchi depth, chlorophyll) and internal correlations among dose and sensitivity parameters. Limitations and advantages with the ecometric approach are stressed.
Time series analysis of the secondary clarifier
Authors: Ossenbruggen Paul J. , Hill Robert D. , Gaudard Marie 
Published: February 2007

AbstractFull scale tests were performed to determine the effects of transient and return flows on the clarification process of activated sludge treatment. An ARIMA‐intervention model and a feedback‐feedforward control scheme for return flow was developed. Computer simulation runs were used to test the effectiveness of the control algorithm. The paper describes and discusses the model identification, the controller development and the controller's potential in application.
Models and their effect on regulation processes
Authors: Phatarfod R. M. 
Published: February 2007

AbstractIn many problems of regulation of water quantity and quality, a process of fundamental importance is that of cumulative sums — weighted or otherwise — of random variables forming a stochastic process. It is commonly believed that in such situations, the structure of the stochastic process is not important as long as the most important parameters remain the same. This means, for example, for a Markov process, with the same stationary distribution and the same serial correlation coefficient, the behaviour of the cumulative sums would be nearly the same. In this paper we show, by analytical methods and by simulation that this belief is erroneous, and that the structure of the input model has a significant effect, and that this effect increases with the serial correlation coefficient.