bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interim analysis of failure time data — A Bayesian approach
Authors: Bartolucci Alfred A. , Katholi Charles R. , Birch Robert 
Published: February 2007

AbstractSince the final results of a comparative intervention (environmental or clinical) trial are usually in doubt at an interim analysis, it is necessary to quantify the uncertainty associated with data yet to be observed. A natural way of quantifying such uncertainty is the Bayesian predictive distribution. The use of such a distribution requires an investigator to specify his opinions in the form of a prior distribution. We reference how this distribution may be obtained by eliciting opinions from an investigator. Further, the methodology is applied to determine an appropriate stopping rule in an experiment to compare survival times of two treatments. This is done by deriving the probability that the ratio of mean or total survival times given the current sample size exceeds a given fixed value. If this probability is below a certain specified value, a dynamic estimate of the required additional sample size may be obtained. We show examples from cancer clinical trials.
Is the incidence of congenital heart defects influenced by the radioactive contamination of the environment due to the accident in chernobyl? — Results from an epidemiological survey on congenital malformations in the South‐German State of Bavaria
Authors: Frasch Gerhard A. 
Published: February 2007

AbstractThe accident in the nuclear power plant at Chernobyl led to a fall‐out and wash‐out of radionuclides, especially radiocesium, with a broad geographic variation in the South‐German State of Bavaria. For a younger adult, the additional effective lifetime dose from external exposure and ingestion of radiocesium varies between 1.2 mSv and 8.7 mSv, depending on the region. This is about 2%, respectively 16%, of the natural lifetime gamma dose. In none of the Bavarian regions with different levels of radiation exposure has the incidence of congenital heart defects in Bavarian children increased since the time of the accident. The results indicate that geographically no effects on such malformations can be attached to the additional radiation exposure resulting from the Chernobyl accident.
S‐system modelling of complex systems with chaotic input
Authors: Voit Eberhard O. 
Published: February 2007

AbstractEnvironmental systems are characterized by large numbers of constituents and processes at hierarchical levels of organization. These levels range from elemental chemical and physical phenomena to multi‐faceted ecosystems that are subject to natural and anthropogenic influences. The analysis of environmental systems is complicated because the governing processes are usually complex and ill defined. In addition to the structural complexity of the investigated phenomena themselves, environmental systems are difficult to analyze because they are constantly exposed to inputs that appear to fluctuate in a chaotic fashion. S‐system models have the potential to address this situation. They are sets of nonlinear ordinary differential equations that were developed as representations for organizationally complex models, primarily in biology and biochemistry. They are characterized by a mathematical structure that allows efficient symbolic and numerical analysis of key features such as steady states, stabilities, sensitivities, and gains. At the same time, S‐systems are structurally rich enough to capture virtually all relevant continuous nonlinearities. This paper begins with a brief review of two key features of S‐systems: modelling and simulation based on power‐law approximation; and the transformation method of recasting which allows differential equations to be formulated exactly as S‐systems. The paper then discusses how chaotically fluctuating input can be simulated with a recast S‐system based on deterministic chaos and how this recast S‐system can be used as an input module for environmental phenomena that are represented as S‐system models via power‐law approximation.
Seasonal cycles, environmental change and BACI designs
Authors: Roberts E. A. 
Published: February 2007

AbstractThe three largest sewage treatment plants (STP) in Sydney were changed from cliff‐face outfalls to deepwater ones between September 1990 and August 1991. The densities of faecal coliforms at Malabar, a test beach, and at Cronulla, a comparison beach, during the last two years before and the first year after the changeover at Malabar STP were used to study methods of analysing data from this type of study. A factorial‐type analysis of variance based on beaches, years and sampling dates during the year as factors was used to estimate the residual variance and to test for change in level of the densities. The residual variance was estimated from the deviations from a periodic regression of density on day of the year.A covariance analysis of the densities at Malabar with those at Cronulla as covariates gave an appropriate statistical analysis. However, there was little difference between the densities at Cronulla before and after the changeover and the simple difference between the densities at Malabar before and after gave the most efficient estimate of change.
A simple test for detecting non‐linear trend
Authors: El‐Shaarawi A. H. , Niculescu Stefan P. 
Published: February 2007

AbstractA test statistic for detecting the presence of a non‐linear trend in a sequence of independent observations is proposed. The test is useful for determining when the use of the non‐parametric slope estimator as a summary of the trend is appropriate. The asymptotic normality of the test is established and simulation results indicate its accuracy for practical applications. An example is used to illustrate the test.
Transport in porous packings: Statistical characterization of transport, role of fluctuation and data analysis
Authors: Giona Massimiliano , Adrover Alessandra 
Published: February 2007

AbstractThe study of dynamics of transport in porous packings (in particular chromatographic columns) represents an illuminating step towards the statistical and probabilistic analysis of transport phenomena and can be generalized to other transport conditions of environmental interest. This paper addresses the use of Monte Carlo simulation in the analysis of transport in porous media, the local characterization of porosity fields in disordered models of pore networks, and the analysis of dispersion in terms of velocity fluctuations, and presents a simplified treatment of experimental data arising from transport problems.
Statistical evaluation of toxicokinetic data
Authors: Becka M. , Bolt H. M. , Urfer W. 
Published: February 2007

AbstractLegal regulations for dangerous chemicals are based on estimates of risks. Toxic effects of chemicals vary from one animal species to another. Hence, it is important to know about chemicals' toxicokinetics (uptake, distribution, elimination and metabolism) in animals and about their inter‐species differences. Knowledge of quantitative metabolic parameters of different species facilitates a species‐specific toxicological approach to the toxic effects observed. The present paper highlights aspects of metabolism within living organisms using deterministic compartmental analysis. Based on non‐invasive toxicokinetic inhalation experiments with rats, mathematical approaches and non‐linear regression analysis are applied to describe the metabolic characteristics of the biological system, and to estimate toxicokinetic parameters. Pre‐existing data on the hydrocarbon propylene (propene) in Sprague‐Dawley rats were used to determine the rates of uptake and exhalation in this species. Differences in the estimated parameters for the uptake and the exhalation from values reported by other authors using different aproaches indicate an urgent need for further investigations.
Detection of sulphur dioxide and nitrogen oxides by means of the ionization method: Comparison with the UV‐fluorescence method and the chemiluminescence method
Authors: Große Hans‐Jörg , Walden Jari 
Published: February 2007

AbstractComparison measurements between the ionization gas analytical system (AIG‐2) for detecting sulphur dioxide from ambient air and a monitor based on the UV‐fluorescence method were performed. The experiments included both laboratory tests at two laboratories with different calibration devices, and field periods in two different cities. According to the laboratory tests the reproducibility of the AIG‐2 was within 7.5 per cent over two years. During the first measurements the response ratio of the monitors was 1.5 on average for both laboratory and field measurements. After maintenance the response ratio was nearly 1.0 in the laboratory measurements and 0.9 in the field measurements. The drift of the AIG‐2 was 0.89 per cent per month. In addition to the comparison of monitors, the stability of the AIG‐2 also facilitates the comparison of different calibration methods between two laboratories. A maximum difference of 15 per cent and a minimum difference of 2 per cent between the output of the calibration devices of the two laboratories was found. We also discovered that the sample matrices of different calibration devices are different (e.g. moisture, inhomogeneities, particles), which can cause problems even when the calibration concentration is the same. As an example of the versatility.
Relative precision of ranked set sampling: A comparison with the regression estimator
Authors: Patil G. P. , Sinha A. K. , Taille C. 
Published: February 2007

AbstractRanked set sampling (RSS) utilizes inexpensive auxiliary information about the magnitude of the variable of interest in order to rank randomly drawn sampling units before quantifying a selected subset of these units. The units to be quantified are chosen, on the basis of the ranking information, in a way that makes theRSSestimator of the population mean at least as efficient as the simple random sample estimator with the same number of quantifications. TheRSSprotocol does not prescribe or restrict the nature of the outside information that is used for ranking the units; however, a common method is on the basis of an auxiliary variable and, here it is natural to ask how theRSSestimator compares with the regression estimator. This paper compares the performance of the two estimators in four situations depending on whether the ranking is perfect or is on the basis of the auxiliary variable and depending on whether double sampling is or is not used for the regression estimator. With perfect ranking, we find that theRSSestimator is considerably more efficient than the regression estimator unless the correlation between the main variable and the auxiliary variable is quite high (say, |ρ| > 0.85). When the ranking is on the basis of the auxiliary variable, the performance of the two methods is comparable for low correlation (say,|ρ| < 0.85), but the regression estimator is more efficient at higher correlations. The investigation assumes that the main and auxiliary variables jointly follow a bivariate normal distribution; this model is considered to be favourable to the regression estimator since the latter is unbiased under bivariate normality, while theRSSestimator is always unbiased.
Relating sets of variables in environmental studies: The sediment quality triad as a paradigm
Authors: Green R. H. , Boyd J. M. , Macdonald J. S. 
Published: February 2007

AbstractMany environmental studies generate multivariate data, often consisting of two or more conceptually different sets of variables. For example one may wish to relate species abundances to environmental variables. A recent concept gaining popularity in marine benthic impact studies is the “sediment quality triad”, which relates benthic community composition as one set of variables to sediment chemistry (contaminents) as a second set, and to toxicities of sediment to test organisms in the laboratory as a third set. This is conceptually appealing, but the original applications of the concept rely heavily on indices (e.g. diversity indices, toxicity indices) rather than on multivariate statistics. Procedures for testing and describing relationships among variables are reviewed, with emphasis on environmental applications. We describe and exemplify several multivariate statistical approaches to effective application of the sediment quality triad concept, including Mantel's tests and related descriptive analyses; ordination on each set of variables followed by methods to relate ordination axes among the sets; canonical correlation analysis; fitting a general linear model; and cluster analysis on each set of variables followed by a three‐way contingency table analysis.
Potency measures for developmental toxicity
Authors: Ryan Louise 
Published: February 2007

AbstractPotency measures provide a useful means of ranking chemicals, drugs and other substances with respect to the danger they pose to humans. While several authors have proposed estimators of carcinogenic potency, the task of ranking developmental toxicants has received little attention. The purpose of this paper is to develop a quantitative measure of developmental potency, based on a correlated multinominal model that allows for dose effects on foetal death, variation and malformation rates. The proposed estimator is illustrated with data from several studies conducted through the National Toxicology Program.
Rank test of trend when data are incomplete
Authors: Alvo Mayer , Cabilio Paul 
Published: February 2007

AbstractConsider a situation where observations are to be taken at regularly spaced time points, but in fact several of the observations are missing. It is wished to determine whether the data exhibit a trend. When the data set is complete, then a well known rank test is based on Spearman correlation. For the case of missing observations a new test of trend is proposed which is analogous to the Spearman test in the complete case. Comparisons between this new test and the naïve Spearman test, which simply deletes all missing observations, indicate that the new test is more sensitive in detecting trends.
Observation network design for selecting locations for water supply wells
Authors: Pesti Geza , Kelly William E. , Bogardi Istvan 
Published: February 2007

AbstractAn observation network design method, which simultaneously minimizes parameter uncertainty and exploration cost, is developed for locating water supply wells. Observation networks combining wells and geophysical measurements are considered for delineating the ‘best’ areas for wells. An area is considered the ‘best’ where the sustainable pumping rate and the degree of protection for the well are the possible maximum. The level of protection is measured by the travel time for a fixed radius protection zone around a well. The ‘most appropriate’ measurement network is selected based on two often conflicting objectives: (1) minimization of the uncertainty in characterizing the spatial distribution of the expected yield and travel time; and (2) minimization of the observation cost. The accuracy of estimation is characterized by the average and maximum kriging variance for yields and travel times calculated from measured hydrogeologic parameters. The parameters to be measured by an observation network for estimating yields and travel times are layer thicknesses, porosity, hydraulic conductivity and volumetric water content. They may be obtained from direct measurements (e.g. well logs, specific capacity test, and pumping tests) or estimated indirectly from geoelectric measurements. The approach combines two common techniques, geostatistics and multi‐criterion decision making (MCDM), to determine the ‘best’ measurement network. Geostatistics are used to determine the spatial distribution of the estimation variance for yield and travel time calculated from different sources of data. MCDM is utilized to evaluate observation network alternatives based on the estimation variances and the costs associated with them. The methodology is illustrated for a study area located near Ashland, Nebraska.
Analysis of daily precipitation data by positive matrix factorization
Authors: Juntto Sirkka , Paatero Pentti 
Published: February 2007

AbstractA new factor analysis method called positive matrix factorization (PMF) has been applied to daily precipitation data from four Finnish EMEP stations. The aim of the analysis was to investigate the structure of the data matrices in order to find the apparent source profiles from which the precipitation samples are constituted. A brief description of PMF is given. PMF utilizes the known uncertainty of data values. The standard deviations were derived from the results of double sampling at one station during one year. A goodness‐of‐fit function Q was calculated for factor solutions with 1–8 factors. The shape of the residuals was useful in deciding the number of factors. The strongest factor found was that of sea‐salt. The most dominant ions in the factor were sodium, chloride and magnesium. At the coastal stations the ratio Cl/Na of the mean concentrations in the factor was near the ratio found in sea water but at the inland stations the ratio was smaller. For most ions more than 90 per cent of the weighted variation was explained. The worst explained was potassium (at worst c. 60 per cent) which is possibly due to contamination problems in the laboratory. In most factors of different factorizations the anions and cations were fairly well balanced.
An analytical air pollution model for complex terrain
Authors: Tirabassi Tiziano , Rizza Umberto 
Published: February 2007

AbstractAn advanced analytical model which computes air pollution concentrations in complex terrain is presented. Model performances are evaluated using wind tunnel measurements of pollutant concentrations from an elevated source in the presence of a rough hill and a neutrally stable flow and are compared with that of COMPLEX I, the Gaussian model proposed by the US Environmental Protection Agency.
Hypothesis tests and confidence interval estimates for the overlap of two normal distributions with equal variances
Authors: Inman Henry F. , Bradley Edwin L. 
Published: February 2007

AbstractA special case of the overlapping coefficient, the common area under two probability density curves, that has received intermittent attention in the scientific and statistical literature concerns the overlap of two normal distributions with equal variances. Here we consider the problem of constructing tests of hypotheses and interval estimation for the true overlap in this special situation. Direct and conditional tests for the true value of the overlap are discussed. A method of constructing an exact confidence interval estimator for the true overlap is presented. Several alternative methods of obtaining confidence intervals for the true overlap are compared in a Monte Carlo investigation. In an example, we use the normal theory results discussed and an invariance property of the overlapping coefficient to estimate the overlap between two log‐normal distributions from sample data.
Subsampling with mark and recapture for estimating abundance of mobile animal populations
Authors: Jensen A. L. 
Published: February 2007

AbstractMethods for mark and recapture can be applied for subsampling to give flexible sampling designs for wild populations where separate segments can be sampled independently. In area sampling, the area inhabited by a population is separated into quadrates and a sample of quadrates is selected with known probabilities; this insures a random sample of quadrates, but good estimates of abundances in the quadrates would be difficult to obtain for mobile animal populations without application of a method designed for such populations. If mark and recapture is applied in quadrates selected using area sampling, the result is a subsample with variation between quadrates and variation within quadrates. An estimator of population abundance is developed for subsampling using mark and recapture, and important consequences for design of such surveys are discussed.
Multivariate QSARs to model the hydroxyl radical rate constant for halogenated aliphatic hydrocarbons
Authors: Eriksson Lennart , Rännar Stefan , Sjöström Michael , Hermens Joop L. M. 
Published: February 2007

AbstractMultivariate quantitative structure–activity relationships (QSARs) are applied to model the atmospheric persistence of halogenated aliphatic hydrocarbons. The objective is to forecast the rate of reaction between the haloalkanes and the hydroxyl radical in the gas phase. The QSARs are developed in the light of a recently proposed strategy for risk assessment of environmental chemicals, based on multivariate data analysis and statistical experimental design. The QSARs are calibrated using a training set consisting of ten chemicals and different sets of descriptors, namely empirical and qualitative indicator variables and quantum‐chemical descriptors. The predictabilities of the QSARs are investigated by making predictions for 13 additional compounds for which experimental observations exist. Finally, the best obtained set of descriptors is used as a basis for making predictions for 35 non‐tested haloalkanes.
Some river wavelets
Authors: Brillinger David R. 
Published: February 2007

AbstractWavelet analysis is described, and a Haar wavelet analysis is carried out, for time series data on the flow rate of the Nile River at Aswan and also on the stages of the Rio Negro at Manaus. A goal of the analysis is to present a wavelet analysis for some time series data particularly looking for jumps in mean level. The work begins with review of techniques for estimating mean levels in the presence of additive noise and then proceeds to the particular case of wavelets and the construction of so‐called “improved estimates” by shrinkage. The results of the analyses are consistent with earlier ones.
Estimatingdfor long and short memory time series
Authors: Janacek Gareth 
Published: February 2007

AbstractFractional difference models are a useful extension to ARIMA models as they model longer range dependence. We suggest a simple and efficient way of evaluation the fractional parameter and illustrate this on a series of mud layer measurements. As can be seen, the model we derive is both simple and compelling. The method used can also be used to estimate the differencing parameter for conventional ARIMA models and in addition can be used to find ARMA parameters. For a fractional model, once the fractional parameter is known a simple filtering operation allows us to proceed as for an ARIMA model. While this is not quite as elegant as a full likelihood approach, it is straightforward and uses common software tools. The pile‐up effect can also be minimized since we do not make a normal approximation.
Some model‐based inference about global warming
Authors: Solow Andrew R. , Patwardhan Anand 
Published: February 2007

AbstractThe magnitude of the response of global temperature to changes in atmospheric composition depends on a parameter called temperature sensitivity. The value of this parameter is unknown. When temperature sensitivity is estimated by fitting the response of a climate model forced by historical changes in atmospheric composition to the historical trend in global temperature, the estimate is rather low. It has been suggested that this may be due to the suppression of warming by sulphate aerosols, an effect that is difficult to incorporate into model experiments. This paper presents an approach to estimating temperature sensitivity based on historical temperature variability, rather than trend, which circumvents this problem. The results are in close agreement with those based on fitting the trend.