bluuebunny/crossref_metadata_2025 huggingface dataset:
Differential expression of Class 3 and 4 semaphorins and netrin in the lamprey spinal cord during regeneration
Authors: Shifman Michael I. , Selzer Michael E.
Published: February 2007

To explore the role of axon guidance molecules during regeneration in the lamprey spinal cord, we examined the expression of mRNAs for semaphorin 3 (Sema3), semaphorin 4 (Sema4), and netrin during regeneration by in situ hybridization. Control lampreys contained netrin‐expressing neurons along the length of the spinal cord. After spinal transection, netrin expression was downregulated in neurons close (500 μm to 10 mm) to the transection at 2 and 4 weeks. A high level of Sema4 expression was found in the neurons of the gray matter and occasionally in the dorsal and the edge cells. Fourteen days after spinal cord transection Sema4 mRNA expression was absent from dorsal and edge cells but was still present in neurons of the gray matter. At 30 days the expression had declined to some extent in neurons and was absent in dorsal and edge cells. In control animals, Sema3 was expressed in neurons of the gray matter and in dorsal and edge cells. Two weeks after transection, Sema3 expression was upregulated near the lesion, but absent in dorsal cells. By 4 weeks a few neurons expressed Sema3 at 20 mm caudal to the transection but no expression was detected 1 mm from the transection. Isolectin I‐B4 labeling for microglia/macrophages showed that the number of Sema3‐expressing microglia/macrophages increased dramatically at the injury site over time. The downregulation of netrin and upregulation of Sema3 near the transection suggests a possible role of netrin and semaphorins in restricting axonal regeneration in the injured spinal cord. J. Comp. Neurol. 501:631–646, 2007. © 2007 Wiley‐Liss, Inc.
Estimation of the global mean temperature with point gauges
Authors: North Gerald R. , Shen Samuel S. , Hardin James W.
Published: February 2007

This paper considers the mean squared error (MSE) incurred in estimating an idealized earth's global average temperature with a finite number of point gauges located in a specified or stochastic way over the globe. For a class of model earths with rotationally invariant statistics, the MSE formula can be cast into the form of a summation over spherical harmonic indices. The summand factors into a part which depends on the design of the gauge network and a second part which is the degree variance spectrum of the surface temperature field. After presenting this formalism, we provide an example spectrum for the surface temperature field derived from a simple two parameter stochastic climate model defined on the sphere. An example calculation is given for the case of N gauges randomly arranged on the sphere. In addition, the sampling error is computed for some simple regular arrays of gauges as illustrative examples.
Minimum error estimates of global mean temperature through optimal arrangement of gauges
Authors: Hardin James W. , North Gerald R. , Shen Samuel S.
Published: February 2007

This paper considers the minimum mean squared error (MSE) incurred in estimating an idealized earth's global average temperature with a finite network of point gauges located over the globe. We use a spectral MSE formalism to find the optimal locations for N gauges in the problem of estimating the earth's global average temperature. Limiting MSE configurations are obtained as the limiting least error case for randomly distributed samples of size N. Our results suggest that for N greater than about 60, one can obtain estimates such that the amount of measured variance due to sampling error is less than 10%, a result likely to be acceptable to climatologists.
A model for the number and sizes of crevices that can be seen on the exposed surface of submerged rock reefs
Authors: Barry Jon , Wickins J. F.
Published: February 2007

A simple model was devised to estimate the number and sizes of crevices visible at the surface of submerged, artificial rock reefs intended to provide shelter for lobsters and crayfish. The model assumes that the rocks are spherical and that each crevice is formed by the independent packing of three rocks. We use this model to derive the mean and variance of the rock size distribution needed to produce a given mean and variance for a crevice size distribution. These results are used as the initial estimates in an algorithm to choose the rock size distribution which comes closest to producing a crevice size distribution appropriate for the creation of new lobster or crayfish habitat.
Use of geoduck clams to indicate changes in the marine environment of Ladysmith Harbour, British Columbia
Authors: Noakes D. J. , Campbell A.
Published: February 2007

Age composition and average annual growth rates were determined for a geoduck clam (Panope abrupta) population located in Ladysmith Harbour, British Columbia. Acetate peels were made from cross‐sections of the right shell portion of each clam. The number and distances between the annuli in shell cross‐sections were used to determine age and growth. Analysis of the growth measurements suggested the annual growth of individuals within this population in any year t was approximately 80% of the annual growth in year t − 1. This estimate does not appear to be a function of age. An average annual index of standardized geoduck growth was constructed for the period 1907–1980. Abrupt changes in mean annual growth were detected near 1919 and 1962. Intervention analysis was employed to quantify changes in mean standardized growth at these two points in time. An 8% increase in geoduck mean annual growth was coincident with an increase in mean annual temperature around 1920. A 27% decrease in growth occurred soon after the initiation of log booming and storage in Ladysmith Harbour (about 1960). The results of this study suggest measurements of growth from geoduck shells may be useful in identifying and quantifying changes in the marine environment.
Modelling wastewater treatment plants through time series analysis
Authors: Capodaglio Andrea G. , Novotny Vladimir , Fortina Luigi
Published: February 2007

Time series analysis models are very useful in modelling dynamic systems in science and engineering applications. This class of models is in fact able to represent the dynamic features of physical systems that are subject to often uncontrollable inputs with random components. Wastewater treatment plants are examples of dynamic systems, with inputs (flow, organic loads, etc.) that vary stochastically within more or less wide ranges. The use of stochastic models allows a more detailed representation of the dynamic nature of these systems, while retaining the degree of information contained in most deterministic models.

In this paper, time series analysis applications to wastewater treatment plant modelling are presented and discussed. Both univariate and multivariate stochastic processes are applied to sewage treatment plant data, and the results thus obtained are further analyzed and compared with those from “conventional” deterministic models.

Specifically, the above mentioned models are analyzed with respect to possible application in the daily operation of sewage treatment plants, by virtue of their predictive capacities and relative ease of determination. Furthermore, these models present other attractive features, such as adaptiveness and the possibility of extracting useful information about the system from the analysis of their structure. Adaptiveness refers to the possibility of continuously improving the model's performance as new information about the system is collected by manual or automatic monitoring. Performance of the models herein identified, and possible applications of these models in real systems are discussed.
An interactive hybrid expert system for polar cloud and surface classification
Authors: Rabindra P. , Sengupta S. K. , Welch R. M.
Published: February 2007

An interactive hybrid expert system is developed to classify polar scenes using AVHRR LAC imagery. A total of 183 spectral and textural signatures are generated from which the 20 “best” are chosen using the Sequential Forward Selection procedure. These 20 features are used to populate the working memory of the expert system. A probabilistic neural network is used as the inference engine to make probabilistic estimates of class membership. As part of the inference engine, a sophisticated outlier test is performed to provide a measure of classification confidence.

During a session, the user is provided with an extensive set of on‐screen aids to assist in labelling. The user may modify the knowledge base by adding new samples to existing classes or by including new classes. The expert system provides confidence measures and a distance measure from the proposed class cluster centre. The interactive environment allows the user to test the impact of class labelling upon the knowledge base before new data is entered. For users working with very large datasets and very complex scenes, the integrity of the knowledge base is the primary concern.

A bootstrap method is used to validate classification accuracy. On the basis of 100 bootstrap samples, an overall classification accuracy of 87% is achieved, with a standard deviation of 1%. The result is that much more accurate cloud classification in polar regions now can be made, which will aid us in our monitoring of global climate changes.
An efficient model for computing plume concentration in aquifers
Authors: Ghori S. G. , Heller J. P. , Singh A. K.
Published: February 2007

Accurate monitoring of contaminated groundwater is one of the most important issues in environmental technology. This paper, composed of two main sections, presents an efficient method of computing plume concentration.

In the first section, reservoir rock properties such as permeability are characterized by geostatistical methods for generating spatial property distributions, such as the source point method (SPM), augmented with conditional simulation from the known sample data. In case the data are not available the permeability field is generated from the SPM alone.

In the second section of the paper, the generated random field along with source/sink flow data is used as input in the pressure equation to solve for the Darcy velocity. The Darcy velocity is then used in the convectivedispersion (C‐D) equation to solve for the flow of contaminant in the aquifer. A newly developed numerical method of solving the C‐D equation is used to follow the movements of the front. The numerical scheme recognizes both convection and dispersion. The physical dispersion includes both longitudinal and transverse dispersion along with the molecular diffusion. The new method is numerically stable and avoids any numerical dispersion. The fronts are displaced according to the direction and magnitude of the concentration gradient, rather than merely from the streamlines. There are two main advantages of this model. Firstly, the tracking of fronts gives a better representation of the contaminant movement in the heterogeneous reservoir. Secondly, the time record of the output concentration at the production wells will enhance the prediction of contaminant migration.
On selecting sample sizes and discrete observation times for longitudinal mortality studies
Authors: Hines R. J. O'Hara , Hines W. G. S.
Published: February 2007

Guidelines are developed for the design and execution of grouped longitudinal mortality studies in toxicology so as to obtain useful information without excessive effort. Issues addressed include the selection of a suitable time scale and effects of group size and observation frequency on the chances of successful experimental runs and on statistical efficiency. Also discussed is the preliminary approximate information needed about mortality variability and times of first and final observations. An example illustrates the methods suggested.
On the consistency of the historic temperature record with greenhouse warming
Authors: Solow Andrew R. , Patwardhan Anand
Published: February 2007

The purpose of this paper is to assess the consistency of the historic record of mean global temperature with a climate model of historic greenhouse warming. Because there is no natural parametric alternative to the climate model, goodness‐of‐fit is assessed via nonparametric regression. While the historic record is not consistent with the model with high temperature sensitivity, it is not inconsistent with the model using the best‐fitting temperature sensitivity.
A stochastic model for adsorption in an open flow system
Authors: Argyelan J. , Nassar R.
Published: February 2007

A stochastic model is developed to characterize flow, adsorption and transport of solute molecules through the solid phase in an open flow adsorber. The flow process is depicted as composed of n compartments with forward and backward flow connecting adjacent compartments. To account for adsorption and transport of solute molecules through the porous solid, the solid phase in each compartment is partitioned into an outer layer and an inner layer. The stochastic process model accounts for movement of solute molecules in the liquid phase, for the transfer of molecules between the liquid and the outer layer of the solid phase and for solute movement through the solid phase. From this model, one may predict the residence time distribution of a molecule in the system and the concentration profile of solute molecules in the outlet stream (breakthrough curve) and estimate the adsorption and flow parameters of the process. In addition to its usefulness in engineering treatment systems such as water and air purification, this modelling approach is useful in studying the transport of organic chemicals in the soil.
Interim analysis of failure time data — A Bayesian approach
Authors: Bartolucci Alfred A. , Katholi Charles R. , Birch Robert
Published: February 2007

Since 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

The 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

Environmental 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

The 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

A 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

The 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

Legal 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

Comparison 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

Ranked 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

Many 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

Potency 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

Consider 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

An 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

A 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

An 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

A 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

Methods 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

Multivariate 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

Wavelet 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

Fractional 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

The 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.
Some advances in non‐parametric multiple time series and spectral analysis
Authors: Walden A. T.
Published: February 2007

Multiple 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

Most 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

Transformations 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

A 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

The 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

The 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

Knowledge 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

Consider 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

In 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

We 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.