bluuebunny/crossref_metadata_2025_split huggingface dataset:
The temporal and spatial variability of rainfall power
Authors: Smith James A. , De Veaux Richard D. 
Published: February 2007

AbstractIn this paper a statistical model of raindrop size distributions is used to examine the temporal variability of rainfall power and to develop techniques for estimating rainfall power from observations of rainfall rate and radar reflectivity factor. The model of raindrop size distributions is based on the assumptions that: 1) raindrop arrival rate at the ground can be represented by a Poisson process with randomly varying rate of occurrence and 2) diameters of raindrops have a lognormal distribution with parameters that are time‐varying random processes. The lognormal distribution plays a central role in both characterization of the temporal variability of rainfall power and in state estimation of rainfall power from rainfall rate and radar reflectivity observations. Empirical results are presented using drop‐size data from a number of sites in the United States. Spatial variability of rainfall power is examined empirically using the rainfall power‐reflectivity relationships developed from drop‐size data and radar data from the southern plains region of the United States.
An empirical bayes approach to the Behrens‐Fisher problem
Authors: Duong Q. P. , Shorrock R. W. 
Published: February 2007

AbstractThis paper is inspired by recent work of G.A. Barnard, who treats the Behrens‐Fisher problem from a Bayesian point of view, and provides calculator programs to implement the solutions (including the classical fiducial solution). We review Barnard's work, and generalize his family of priors in a manner that makes it convenient to estimate the form of the prior from the observed variance ratio. A calculator program is provided to give an empirical estimate (based on marginal maximum likelihood), in an interesting special case, of a parameter of the prior distribution. In addition, programs similar to Barnard's are provided to calculate Bayes or Empirical Bayes p‐values and confidence intervals.
Multistate survival models for partially censored data
Authors: Ataharul Islam M. , Singh Karan P. 
Published: February 2007

AbstractMultistate survival models for partially censored data are of great interest for investigating factors attributing to transitions from one state of survival to other states of survival or death. Kay (1982) extended the proportional hazards model for a multistate framework with several transient states. However, in his framework, Kay did not incorporate reverse transitions. This paper discusses an extension of the proportional hazards model for several transient states under a competing risk framework for transitions and reverse transitions. The relationship with the multistate product‐limit method is also shown in this paper.
Estimating the effect of emissions strategies on wet deposition of sulfates
Authors: Stein Michael L. 
Published: February 2007

AbstractThe computer model RADM (Regional Acid Deposition Model) has been developed to study the effect of emissions reduction strategies on acid deposition. By running RADM on past meteorology under both present and proposed emissions, it is possible to assess the impact of a change in emissions on acid deposition. Combining these computer simulations with daily monitoring data available at select locations allows for much more precise and possibly much less biased estimation of the effect of the change of emissions on wet deposition than just using the computer simulations. This paper investigates changes in wet sulfate deposition, although the same procedures can be applied to any other component of precipitation computed by RADM and measured at least daily at a monitoring site.
Chronic disease surveillance and testing of clustering of disease and exposure: Application to leukemia incidence and TCE‐contaminated dumpsites in upstate New York
Authors: Waller Lance A. , Turnbull Bruce W. , Clark Larry C. , Nasca Philip 
Published: February 2007

AbstractSurveillance of large geographic areas for “clusters” of adverse health events, particularly cancers, has interested statisticians and epidemiologists. Recent statistical research involves so‐called “focused” clustering of disease, i.e. aggregation of incident cases of disease in the vicinity of prespecified, putative foci of increased risk. We introduce a statistical test of focused clustering with optimal, or near‐optimal power properties. This test, and two other recent tests of focused clustering are illustrated and compared using leukemia incidence data for 1978–1982 in a region of upstate New York, with inactive hazardous waste sites containing trichloroethylene acting as the suspected foci. Once potential problem areas are identified, more localized methods may be applied.
Assessing attainment of ground water cleanup standards using modified sequential t‐tests
Authors: Roger John 
Published: February 2007

AbstractAssessing the attainment of Superfund cleanup standards in ground water can be complex due to measurements with skewed distributions, seasonal or periodic patterns, high variability, serial correlations, and censoring of observations below the laboratory detection limit. The attainment decision is further complicated by trends and transient changes in the concentrations as a result of the cleanup effort. EPA contracted Westat to prepare a guidance document recommending statistical procedures for assessing the attainment of ground water cleanup standards. The recommended statistical procedures were to require a minimum of statistical training. The recommended procedures included a sequential t‐test based on yearly average concentrations.Further research and simulations by Westat indicate that modifications of the sequential t‐test have better performance and are easier to use than the originally proposed sequential t‐test, particularly with highly skewed data. This paper presents three modified sequential t‐tests with simulation results showing how the sequential t‐test and the modifications perform under a variety of situations similar to those found in the field. The modified tests use an easy‐to‐calculate approximation for the log likelihood ratio and an adjustment to improve the power of the test for small sample sizes. Using the log transformed yearly averages improves the test performance with skewed data. Expected sample sizes and practical considerations for application of these tests are also discussed.
Statistical methods for assessing environmental effects on human genetic disorders
Authors: Piegorsch Walter W. , Taylor Jack A. 
Published: February 2007

AbstractMethods are presented for assessing interactions and other effects between genetic and environmental factors for human disease or cancer susceptibility. Statistical estimation and testing approaches are based on a simple multinomial sampling model for the case‐control sampling scenario. It is noted that logistic regression methods can facilitate computation of likelihood‐based statistics in this setting. Additional models for collapsibility over genotypes within the genetic factor are considered. Monte Carlo comparisons show that the method appears to retain nominal significance levels at total sample sizes above 100. At smaller sample sizes, a goodness‐of‐fit statistic is suggested for testing the interactive effect between the genetic and environmental factors.
On kendall's tau as a test of trend in time series data
Authors: El‐Shaarawi A. H. , Niculescu Stefan P. 
Published: February 2007

AbstractKendall's tau is often used to detect the presence of trend in environmental time series data. Assuming stationarity, a general expression for the variance of the associated S score is derived. The results are specialized to the cases of MA(1) and MA(2). Asymptotic normality of tau is established. It is shown that the variance of tau is strongly affected by the assumption of statistical dependence. A seasonal model with non‐zero correlations between successive seasons and years is considered. Simulation results indicate that the effects of departures from distributional assumptions are less important when compared to the effects of departures from the independence assumption.
Global trend of acid wet deposition — a parametric modelling of sulfate concentrations
Authors: Liu Jian 
Published: February 2007

AbstractIn this report, we examine acid wet deposition data collected sequentially in time at multiple monitoring stations. The key objective here is to give a reasonable description of the overall trend pattern of the wet deposition of sulfate. We proceed by first modelling the data collected at each individual station to obtain individual trend estimates at that particular station and then pooling together these trend components by means of a multiple random effects model. Our conclusion is that the overall trend in the United States exhibits a U‐shape.
Simulation of coral reefs and crown‐of‐thorns starfish
Authors: Crimp O. N. , Braddock R. D. 
Published: February 2007

AbstractCoral reefs are subject to attack and predation by the starfish Acanthaster planci or crown‐of‐thorns. This paper describes a simulation model of the natural processes of a coral reef and its reaction to, and recovery from starfish attacks. The modelling of a reef section is based on a pixel representation of the area. On this are imposed the normal reef processes of coral recruitment, growth, competition and mortality. These were represented in computer form and a suitable computer package was produced and tuned to represent a typical hard coral mid‐shelf reef of the Great Barrier Reef. Fine tuning required the inclusion of a background mortality to ensure that the output extended over centuries, and exhibited the essential properties of real coral communities.The second phase of the simulation was to model the effects of a starfish attack, its likelihood of occurrence, strength and dietary preferences. Again, these may be tuned to represent the before and after effects of attacks as observed on the reef.The results of the simulations lead to the conclusion that the modelled coral community exhibited considerable stability (persistence and resilience) in the face of frequent and severe Acanthaster outbreaks. Regardless of the outbreak intensity, the coral community rapidly regained its pre‐outbreak cover of the area in about eight years. Some changes in the community composition were found in the periods immediately after outbreaks. However, the pre‐outbreak composition of the community was always restored within 20–30 years.
Nutrient trends in the Baltic Sea
Authors: Sandén Per , Rahm Lars 
Published: February 2007

AbstractThe Baltic Sea has been subject to an increased load of nutrients during the last century. The effect of this load is obscured sometimes by a lack of data but always by large seasonal variations. A non‐parametric method was used for trend tests at nine different sampling stations from the whole Baltic Sea. Time series from 1968–1990, based on data from HELCOM and ICES databases, were analyzed for trend in concentrations of NO3, NH4, PO4, total phosphorous and total nitrogen.About 60% of the test statistics gave significant trends at 5% confidence levels. Total nitrogen, total phosphorous, NO3, and PO4 have generally increased in the Baltic Sea. The increase is in the range of 1–4% yr−1. Ammonium shows decreasing trend in the Baltic Sea, with a slope of 3–8% yr−1. Regional differences are, however, found for most of the constituents.
Score tests for the single‐hit poisson model in limiting dilution assays
Authors: Cyr Louis , Singh Karan P. 
Published: February 2007

AbstractWhile estimators of the relative frequency of responding cells in limiting dilution assays (LDAs) have been critically evaluated, other statistical issues concerning this quantal assay have not received similar attention. This study investigates LDA validity tests designed to detect deviations from the single‐hit Poisson model. The method employed by Armitage (1959) to detect host variability, or a variable number of false negatives, is extended to other violations of the single‐hit Poisson model. In general, this methodology, based on the general theory of Neyman (1955), is shown to produce asymptotically most powerful and locally most efficient tests for many alternative hypotheses. In addition to composite hypotheses, this method can be extended to produce tests for separate families of hypotheses. These methods are compared with existing LDA validity tests including the weighted regression method of Gart and Weiss (1967), the double‐hit model of Cox (1962), and the general chi‐square goodness‐of‐fit statistic. Monte Carlo simulations show that these score tests have good power and acceptable rejection levels at 0.05 and 0.01. In particular, the score test designed to detect deviations from the Weibull model is shown to produce good power for many alternative models. The chi‐square goodness‐of‐fit statistic, the most widely used LDA validity test, is shown to lack power in detecting many alternative hypotheses.
Design‐based versus model‐based estimates of spatial means: Theory and application in environmental soil science
Authors: Brus Dick J. , DeGruijter Jaap J. 
Published: February 2007

AbstractThe perspectives and concepts of the classical sampling or design‐based approach and the geostatistical or model‐based approach are compared. We show that unbiasedness and minimum variance in the design‐based approach is quite different from that in the model‐based approach and that design‐based strategies are always valid, whether or not there is spatial autocorrelation. Model‐based predictions of spatial means will generally not have the desirable property of unbiasedness in the design‐based sense. Model‐based strategies contain a risk arising from biased selection and they do not allow the accuracy of predictions to be assessed objectively, i.e., based on the sample data alone. Model‐based strategies are useful for local estimation, i.e., for many small blocks and points, provided that there are enough data to estimate the variogram. In a case study on phosphate saturation, design‐based and model‐based estimates of the areal fractions saturated with phosphate were similar, but with smaller blocks the differences increased to magnitudes of practical importance.
Grey box modelling in two time domains of a wastewater pilot scale plant
Authors: Carstensen J. , Madsen Henrik , Poulsen Niels Kjølstad , Nielsen Marinus K. 
Published: February 2007

AbstractWastewater treatment processes are characterized by the existence of many different biochemical and physical processes, which partly can be controlled through operation of the wastewater treatment plant. Time series models of these processes are very useful for gaining insight into real time operation of wastewater treatment systems which deal with variable influent flows and pollution loads. In this paper nonlinear time series models based on knowledge of the processes are established. Rates of the biochemical and hydraulic processes are identified, and by statistical methods these constants for the biochemical processes are estimated assuming Monod kinetics. The models are solely based on on‐line measurements of ammonia and nitrate concentrations and corresponding controlling signals from a wastewater pilot scale plant. The models only include those hydraulic and kinetic parameters which have been shown to be significant in a statistical sense, and hence can be quantified.
Statistical extrapolation of nutrient concentrations in the Baltic Sea
Authors: Nordgaard Anders , Hjorth Urban 
Published: February 2007

AbstractIn this paper we present some methods for extrapolating nutrient concentrations in the water mass of the Baltic Sea. One purpose is to find the total amounts of nutrients in different basins. Data are modelled as observations from a stochastic process having room coordinates as its parameters. The extrapolation is treated as a statistical prediction procedure divided into two steps. The first step estimates the mean of the concentration at the point to which the extrapolation is made. The second step adds to the estimated mean a linear combination of residuals between estimated means and actual response values in surroundingdatapoints. The coefficients in the linear combination and the number of surrounding points included are decided according to an estimated correlation model. The two steps both include heavy computer calculations consisting of model selection procedures and non‐linear programming algorithms. Three reasonable methods may be deduced from the two steps depending on how we estimate the mean and how correlated the residuals from the estimated means seem to be. The choice of method is based on validation towards the current data set. The extrapolated concentrations have also been integrated over the water volumes to obtain estimates of total amounts of the nutrients. Estimates from the three methods above have been compared.
Displaying inter‐ and intra‐regional variability of large‐scale survey results
Authors: Posch Maximilian , Kämäri Juha , Johansson Matti , Forsius Martin 
Published: February 2007

AbstractCurrently many European nations are taking part in a coordinated exercise on mapping critical loads of air pollutants for aquatic and terrestrial ecosystems. A statistically based lake survey and soil analyses from sites uniformly covering the country are used to assess the critical loads of sulphur and nitrogen for lakes and forest soils in Finland. When presenting results based on such large and spatially extensive data sets one has to take into account the user of this information. For the scientific community it is desirable to retain as much information as possible in the display of the results, whereas clarity and simplicity of presentation are crucial for the understanding and acceptance by policy‐makers. In this paper we present different methods for displaying spatially varying information, discuss various statistical descriptors for characterizing distributions by a few numbers, and compare the influence of alternative ways of aggregating data on the final display of results.
Trend analysis methods for environmental data
Authors: Esterby S. R. 
Published: February 2007

AbstractRecently, the heightened interest in the assessment of the current state of environmental conditions and the detection of change in environmental conditions has led to a corresponding interest in statistical trend assessment methods. In the present paper, the analysis for trend is considered as part of the larger task of characterizing the variability of an environmental quality indicator when data are available from a monitoring programme. The features of several parametric and non‐parametric methods are discussed with respect to methods of accounting for seasonality, inherent models for trend, the ability to handle changes of different forms, the inclusion of concomitant variables in the analysis and the assumptions of the method. Examples of the analysis of water quality data using these methods are given.
Biometrical methods for testing dose effects of environmental stimuli in laboratory studies
Authors: Piegorsch Walter W. 
Published: February 2007

AbstractIn the areas of toxicology and environmental biology, the damaging effects of environmental chemicals or other stimuli on biological systems are often studied in controlled laboratory experiments. These usually involve animal and microbial systems. Data from such experiments are analysed via various statistical approaches, depending on the nature of the endpoint and aspects of the particular assay under study. Major endpoints of interest include carcinogenicity, mutagenicity, and teratogenicity. When the environmental stimulus is administered over a range of doses, it is of interest to estimate and/or test features of the dose response. In some instances, variance heterogeneity and overdispersion are present, and adjustments to the statistical methods are required. Herein, such methods for assessing dose response for the major endpoints noted above are discussed, with emphasis directed at testing for an increasing dose response.
Compliance testing of two or more water quality determinands using quantiles
Authors: Cotter A. J. R. 
Published: February 2007

AbstractThe testing of k > 1 water quality determinands for compliance with environmental regulations leads to difficulties in finding the probabilities of wrongly concluding (a) that one or more determinands failed when in fact all passed (joint type I error), and (b) that one or more passed when all failed (joint type II error). Also, owing to dependence among the determinands, some may contribute little to the compliance assessment and, if they serve no other purpose, are a waste of analytical resources. A method based on sample quantiles is suggested for estimating the theoretical number f of completely independent, notional determinands whose information content is equivalent to the k actual determinands being regulated. Next, the alternative strategies of individual and collective testing of the k determinands against regulations set as quantiles are compared in terms of joint type I and II error probabilities. The extreme cases of negative and positive dependence among the determinands set ranges for these probabilities, but even with moderate k they tend to be impractically broad for reliable decision making with either test strategy. Assuming independence as a worst case instead of negative dependence helps somewhat. It is explained how, alternatively, point estimates for the error probabilities are possible using f, given certain assumptions. Two schemes for regulating multiple determinands thus emerge, one based on an agreed worst‐case situation which places minimal reliance on assumed conditions, the other based on an estimated error situation utilizing f. The choice between them would be influenced by the rigour required of the tests. The relative performance of individual and collective testing is shown to depend on the parameters chosen for the tests, and the degree and sign of dependence among the determinands. The design of compliance testing schemes is crucial in determining whether most risk lies with waste producers or the receiving environment, and what effect the prescribed regulations will actually have in practice.
The analysis of radon survey data
Authors: Schwenke James R. , Butine Michael D. 
Published: February 2007

AbstractObserved radon gas levels typically demonstrate a skewed sampling distribution. Traditional analyses to compare mean or median radon levels among different demographic groups rely on transformations of these data. The logarithmic transformation is one that is traditionally employed. Unfortunately, transformations such as the logarithmic tend to de‐emphasize extreme values. In radon gas research, these extreme values are of primary interest. The proposed methodology is based on characterizing the sampling distribution of radon levels by a non‐normal distribution. The mean radon level is estimated based on the assumed sampling distribution. Comparisons among groups are considered through a weighted analysis of variance. Results of a survey conducted by the state of Kansas are used as an example.