bluuebunny/crossref_metadata_2025_split huggingface dataset:
Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data values
Authors: Paatero Pentti , Tapper Unto 
Published: February 2007

AbstractA new variant ‘PMF’ of factor analysis is described. It is assumed that X is a matrix of observed data and σ is the known matrix of standard deviations of elements of X. Both X and σ are of dimensions n × m. The method solves the bilinear matrix problem X = GF + E where G is the unknown left hand factor matrix (scores) of dimensions n × p, F is the unknown right hand factor matrix (loadings) of dimensions p × m, and E is the matrix of residuals. The problem is solved in the weighted least squares sense: G and F are determined so that the Frobenius norm of E divided (element‐by‐element) by σ is minimized. Furthermore, the solution is constrained so that all the elements of G and F are required to be non‐negative. It is shown that the solutions by PMF are usually different from any solutions produced by the customary factor analysis (FA, i.e. principal component analysis (PCA) followed by rotations). Usually PMF produces a better fit to the data than FA. Also, the result of PF is guaranteed to be non‐negative, while the result of FA often cannot be rotated so that all negative entries would be eliminated. Different possible application areas of the new method are briefly discussed. In environmental data, the error estimates of data can be widely varying and non‐negativity is often an essential feature of the underlying models. Thus it is concluded that PMF is better suited than FA or PCA in many environmental applications. Examples of successful applications of PMF are shown in companion papers.
A seasonal Markov model for extremely low temperatures
Authors: Coles Stuart G. , Tawn Jonathan A. , Smith Richard L. 
Published: February 2007

AbstractTime series of temperatures during periods of extreme cold display long‐term seasonal variability and short‐term temporal dependence. Classical approaches to extremes circumvent these issues, but in so doing cannot address questions relating to the temporal character of the process, though these issues are often the most important. In this paper a model is developed with the following features: periodic seasonal effects; consistency with asymptotic extreme value theory; Markov description of temporal dependence. Smithet al.studied the properties of such a model in the stationary case. Here, it is shown how such a model can be fitted to a non‐stationary series, and consequently used to estimate temporal aspects of the extremal process of low temperatures which have most practical and scientific relevance.
A space‐time analysis of ground‐level ozone data
Authors: Guttorp Peter , Meiring Wendy , Sampson Paul D. 
Published: February 2007

AbstractWe examine hourly ozone data collected in connection with a model evaluation study for ozone transport in the San Joaquin Valley of California. A space‐time analysis of a subset of the data, 17 sites concentrated around the Sacramento area, indicates a relatively simple spatial covariance structure at night‐time, while the afternoon readings show a more complex spatial covariance, which is partly explained by observations from a single station with suspicious data. Simple separable space‐time covariance models do not appear applicable to these data.
From data and theory to environmental model: The case of rainfall runoff
Authors: Jakeman Anthony J. , Post David A. , Beck M. Bruce 
Published: February 2007

AbstractModels of environmental systems, constructed for the purpose of predicting or understanding the effects of input changes, fall predominantly into two classes: those based on idealized equations of mathematical physics, and those based on compartmentalized conceptual descriptions of processes. Overparameterization is often a common feature of both these approaches. System identification offers the opportunity to begin model construction with simple structures and assumptions, and to build up the level of model detail by testing refinements for their consistency with system observations. We use rainfall runoff modelling to demonstrate this approach. We also use it to argue that model regionalization is a powerful technique to help generate better understanding and prediction of environmental systems. The construction of identifiable models which are transferable within a ‘region’ of similarity makes the exercise more tenable, and facilitates the jump to more generic models applicable in a wider (e.g. global) context.
Prediction of surface water turnover time in coastal waters using digital bathymetric information
Authors: Persson Johan , Håkanson Lars , Pilesjö Petter 
Published: February 2007

AbstractThis paper presents a new type of geographical information system (GIS) for scientific planning of coastal waters. One hypothesis in the work is that the morphometry of the coast plays a significant role for how the water system functions as a receiving water system, for example, as a receiver of industrial and urban pollution and in response to various forms of aquaculture. A digital technique for transferring information from standard charts into morphometric parameters expressing various characteristics of the coast has been developed. Empirical data on surface water turnover times, which are costly and demanding to determine with traditional hydrodynamic methods, has been obtained from the literature for 20 defined Swedish coastal areas. Two models for simple predictions of the median surface water turnover time, based on morphometric parameters, have been developed to exemplify the use of “morphometrical models” in expressing a coast ecological key parameter. In these models, more than 90 per cent of the variation in empirical values of surface water turnover times can be statistically explained by the topographic openness. The topographic openness describes the exposure of the coastal area towards the open sea or adjacent coastal area. The models are valid for the temperature stratified period (May–October) in areas non‐affected by tides, strong coastal currents and river inflow. The areas should also be in the size range 0.15–150 km2.
Forecasting hurricane tracks using the Kalman filter
Authors: Bril Gordon 
Published: February 2007

AbstractA case study applies state space models and the Kalman filter to forecasting the path of hurricanes. The track of a hurricane is forecast by using its previous motion together with the historical record of storms which follow a similar path. Criteria are described for selecting the predictor storms, which are then incorporated into the measurement equation of the state space model. Linear and quadratic trend structural time series models are used in the transition equation of the state space model and forecasts are produced using the Kalman filter. Forecasts are obtained for hurricanes Andrew (1992), Bob (1991), Hugo (1989) and Gilbert (1988). The Kalman filter approach compares very favourably with the HURRAN and CLIPER benchmark models used by the National Hurricane Center.
Statistical analysis on markov‐modulated poisson processes
Authors: Ramesh N. I. 
Published: February 2007

AbstractA class of doubly stochastic Poisson processes, which is termed a Markov‐modulated Poisson process, is studied. The maximum likelihood method is used to make inferences about the Markov‐modulated Poisson process. Expressions are derived for the likelihood function and for second‐order properties of both counts and intervals. A simple two‐state model is applied to a set of exposure data and to simulated data. Bivariate generalization of this process is also studied.
A minimum cost estimator for the mean of positively skewed distributions with applications to estimation of exposure to contaminated soils
Authors: Chen Ling 
Published: February 2007

AbstractIn this paper, a new flexible cost function is introduced in order to reduce the probability of underestimating the mean of positively skewed distributions by using the sample mean. Under this cost function, a minimum cost estimator is derived and its theoretical properties are studied. Simulation studies are based on the lognormal distribution and on a data set of chromium concentration from one of the EPA's toxic waste sites. The results show that the estimator proposed in this article is superior to the upper confidence limit (UCL) estimator currently being used by EPA to alleviate the underestimation problem.
Public health role, aluminium and Alzheimer's disease
Authors: Smith Lesbia F. 
Published: February 2007

AbstractThe recent literature has given considerable attention to the role of aluminium (Al) in Alzheimer's disease (AD) and to the possible etiological link of ingestion of aluminium from public drinking water supplies. Principles on prevention espoused by public health oblige it to define its role in modifying exposure to aluminium if such exposure is considered a significant risk factor in the causation of AD. Some researchers recommend public education about the suspected link between aluminium and AD, the labelling of food and other products for human use with the content of aluminium, and the reduction of aluminium in alum‐treated drinking water to less than 100 μg/l. Implementation of these recommendations could require significant changes in public health policy and in individual behaviour. AD is an important health problem. Any reasonable and practical measures which would prevent AD are worthy of public policy support. However, public health policy should be based on scientific information and not on speculation.
The ability of wet deposition networks to detect temporal trends
Authors: Oehlert Gary W. 
Published: February 2007

AbstractWe use the spatial/temporal model developed in Oehlert (1993) to estimate the detectability of trends in wet‐deposition sulphate. Precipitation volume adjustments of sulphate concentration dramatically improve the detectability and quantifiability of trends. Anticipated decreases in sulphate of about 30 per cent in the Eastern U.S. by 2005 predicted by models should be detectable much earlier, say, 1997, but accurate quantification of the true decrease will require several additional years of monitoring. It is possible to delete a few stations from the East without materially affecting the detectability or quantifiability of trends. Careful siting of new stations can provide substantial improvement to regional trend estimation.
Goodness‐of‐fit using very small but related samples with application to censored data estimation of PCB contamination
Authors: Johnson Richard A. , Gan D. Robert , Berthouex P. M. 
Published: February 2007

AbstractMany environmental data sets contain observations which are below the method or instrumental limit of detection which are often referred to as ‘censored data’. These censored data create great uncertainties in the data analysis for the environmental quality management. It is necessary to quantify these uncertainties in order to determine compliance, or non‐compliance, with relevant environmental quality standards, to estimate the parameters used in assessment models, and to monitor the environmental quality. The analyses for data sets that contain censored data are not new, but most distribution free methods require large sample sizes. For a data set containing censored observations with a small size, e.g. n = 3, which is typically observed for environmental samples, distribution free techniques will fail to provide reasonable answers due to the tremendous uncertainties in the analysis. This paper is proposed to demonstrate one approach which could be used to analyse environmental data sets with small sample sizes. If a distribution is known to be normal, or log‐normal, optimal estimates are easily obtained. In the scientific investigation that motivated this study, at most three observations are available. We address two statistical questions. The first concerns normality. If all the populations are normal, even with different means and variances, we can obtain an overall check of fit. To do so, we propose to calculate the correlation between the three order statistics and the normal scores. The distribution of the whole set of correlations is then compared to what it would be if all of the populations are normal. Secondly, we consider transformations of the original observations and illustrate how our goodness‐of‐fit procedure can be used to select a transformation.
Imputation of missing values in spatio‐temporal solar radiation data
Authors: Glasbey C. A. 
Published: February 2007

AbstractSolar radiation was measured almost every 10 minutes for 2 years at ten sites in the Pentland Hills to the south of Edinburgh. A few values were missing because of instrument failure or because the data logger was occupied in data transfer. Two ad hoc methods for replacing the missing values are compared by cross‐validation. It is found to be better to interpolate sequences of up to four missing values, and the first and last missing values in longer sequences, using data from the same site, but otherwise it is better to average synchronous data from the other nine sites. A spatio‐temporal model of the second‐order moments of the data is identified and used to specify optimal linear predictors based on both within‐ and between‐site data.
Turbulent dispersion and the beta distribution
Authors: Chatwin P. C. , Lewis D. M. , Sullivan Paul J. 
Published: February 2007

AbstractSome recent research on the properties of the probability density function for the concentration of a scalar dispersing in turbulent flow is summarized. It is concluded that the beta distribution may be a simple model consistent with these properties. This proposal is verified theoretically, and some preliminary data comparisons are very promising.
Uncertainties in lung cancer risk estimates reported for exposure to environmental tobacco smoke
Authors: Gross Alan J. 
Published: February 2007

AbstractThe United States Environmental Protection Agency (EPA), in its recent risk assessment dealing with environmental tobacco smoke (ETS), estimated the annual number of lung cancer deaths in non‐smokers (including both never‐smokers and former‐smokers who have quit smoking for at least five years) associated with ETS exposure to be between 2500 and 3300 in the United States. This estimate is based on a number of assumptions that are not supported by the existing data. Moreover, the range presented by EPA fails to take into account the uncertainties involved in this estimate. This paper discusses the significant assumptions that underlie any quantitation of lung cancer risks allegedly due to ETS exposure, and explores the uncertainties of each assumption and how each uncertainty affects the summary estimate. The author concludes that any range 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 α‐β model for concentration moments in turbulent flows
Authors: Mole Nils 
Published: February 2007

AbstractChatwin and Sullivan (1990a) proposed a simple model (in terms of two parameters α and β) for the moments of scalar fluctuations in self‐similar turbulent shear flows. They showed the model results to be well satisfied by observations from a range of experiments. Here their theory and some developments from it are summarized. The latter include results on the relationships between higher moments, in particular the skewness and kurtosis, and models for the evolution of α and β.
A generalized pareto distribution model for high concentrations in short‐range atmospheric dispersion
Authors: Mole N. , Anderson C. W. , Nadarajah S. , Wright C. 
Published: February 2007

AbstractGeneralized Pareto distribution (GPD) models are fitted (by maximum likelihood) to extreme concentrations in a set of atmospheric dispersion experiments. QQ plots, together with simulations to provide an objective measure of goodness of fit, are used to show that these models fit the data well. It is found that in some cases a model in which the GPD parameters vary with time, in particular in a periodic manner, gives a significant increase in the likelihood over that for the model with constant parameters. Predicted return levels decrease with increasing downwind distance from the source. They are also higher in the experiments in stable conditions than in those in convective conditions, although it is argued that this might well not have been the case had the duration of the stable experiments been greater.
Modelling the evolution of moments of contaminant concentration in turbulent flows
Authors: Clarke Liz , Mole Nils 
Published: February 2007

AbstractSullivan and Moseley developed a model for the evolution of concentration moments in turbulent flows. It utilized Chatwin and Sullivan's α‐β model and the space‐integrated advection‐diffusion equation with a closure assumption. The resulting model gave generally realistic results in one spatial dimension, but gave less satisfactory results in two and three dimensions. Here this is shown to be associated with a singularity in the equations which is independent of the closure assumption. However, the singularity occupies a non‐physical region of the phase space, and an alternative closure is presented which avoids this region and yields physically realistic solutions in one, two and three dimensions.
Turbulent diffusion as a stochastic lagrangian process
Authors: Rodean Howard C. 
Published: February 2007

AbstractThe present status of stochastic Lagrangian models of turbulent diffusion is reviewed, beginning with a brief historical introduction. The mathematical and physical foundations of these models are described. The derivation of the model for the Lagrangian trajectory of a single particle is illustrated for one‐dimensional inhomogeneous Gaussian turbulence. Single‐particle models for non‐Gaussian turbulence in the convective boundary layer are described. The mean concentration in fixed co‐ordinates is calculated from the statistics of thousands of single‐particle trajectories. Another class of stochastic Lagrangian models gives the relative dispersion of particle pairs and the concentration variance. A list of references is provided for further study.
Statistical fluctuations due to microscale mixing in a diffusion layer
Authors: Zimmerman William B. , Chatwin P. C. 
Published: February 2007

AbstractA new viewpoint on the evolution of scalar dispersion in a turbulent flow is taken by considering diffusion of a passive scalar where the measuring device is stochastically positioned. This is a model of a dispersion process where mixing at small scales occurs by molecular diffusion and large scale motions are rigid in regards to the internal cloud structure. Numerical results for the first four central moments are presented, and are shown to be consistent with theoretical approximations for both small and large times. The plot of kurtosis against skewness collapses onto a quadratic curve close to those discussed by Mole and Clarke, and others. The present work suggests that this robust feature has a rather simple basis in physics, and further research is proposed that will critically test this interpretation.
Statistics and the environment: The analysis of large‐scale earth‐oriented systems
Authors: Thiébaux H. Jean 
Published: February 2007

AbstractThe focal point of this paper is the concept: the environmental unit is Earth itself. To analyze and predict global environmental phenomena, the dimensions of our domain of concern must match those of the planet's dynamic fluid envelope: atmospheric and oceanic. Traditional techniques of statistical diagnostic analysis and prediction must be cast in the natural coordinates of earth‐oriented systems and computer technology tasked to its present limits to handle global scale data bases.A notation is presented in which various approaches to estimating variables in large domains can be seen as adaptations to a common estimation objective. Their differences arise from differences in the scope and extent of information available to describe the system under study. The “universal estimator notation” serves to identify areas for significant further development. Examples are given which illustrate the potential impacts on spatial estimation algorithms of using available data to tune them to location and current state.
An examination of the lognormal and box and cox family of transformations in fitting environmental data
Authors: Stoline Michael R. 
Published: February 2007

AbstractThis paper investigates the fit of the lognormal model and Box and Cox (BC) family of transformation models to several data sets that arise in environmental contexts where the data may be heavily left‐censored with multiple detection limits. A model fitting protocol is described that selects an optimal BC model, identifies outlying observations, and verifies the final model using a normal probability plot correlation statistic. This protocol is used to fit these models to data collected on seventeen groundwater chemistry variables in Ingham County, Michigan in 1986‐1987 as part of the Michigan Groundwater Survey. Six of these variables are adequately modelled by an optimally‐chosen BC model and five of these six are adequately modelled by the lognormal model. Ten of the study variables are marginally modelled by a BC model, whereas only six of the study variables are marginally fit by the lognormal model. The remaining variables fail the fitting criteria due to the significance of the probability plot correlation statistic or because of the presence of outlying observations in the final model.On the basis of these results, the data analyst is cautioned against the automatic use of the lognormal model for fitting environmental data in every circumstance. What is recommended is checking the adequacy of the lognormal model prior to use. If the lognormal model is not an adequate model, then perhaps some other member of the Box and Cox family will provide an adequate model.
Determination of sample size during ongoing sampling
Authors: Violato Claudio 
Published: February 2007

AbstractAn empirical technique for determining sample size during ongoing, non‐probability, haphazard sampling is described in the present paper. While there are many techniques for determining sample size a priori, all of them require a knowledge of the population parameters (or at least their variance). Moreover, these a priori methods are based on assumptions of probability sampling such as simple random, stratified random, cluster, and so on. It is common in psychological, biological, social, environmental and medical research, however, to employ non‐probability samples of unknown representativeness and with virtually no knowledge of the parameters in question. Thus a priori techniques are quite impractical and limited in their usefulness.The procedure described in the present paper is an empirical method which does not require assumptions of probability sampling. The method involves determining the point of convergence of Sums of Squares and Cross Products (SSCP) matrices in sequential sampling using Wilk's lambda as a criterion with Rao's approximate F as a test statistic. When the SSCP matrices of two sample sequences converge at the α = .10 level of significance, the sample size is determined to be adequate as the estimators have stabilized. Further sampling would produce redundant data. An application of the present technique to a large scale study is given as a practical example.