Data collected for the investigation of the environmental and ecological characteristics of a river basin are often in the form of a large three-way array; hence, a particular version of the Tucker model could be applied to gather more information contained in such complex geochemical systems. Indeed, when the data are in compositional form, more attention must be given to the analysis of the numerical data. Recently, the Tucker3 model has been proposed to analyze compositional data characterized by a three-way structure. In this work, a particular version of the Tucker model, known as the weighted principal component analysis, was used to analyze water samples collected from the Arno river (Tuscany, central Italy) in order to evaluate the method's effectiveness. Several graphical displays have been developed to allow an accurate and complete interpretation of results. Copyright © 2013 John Wiley & Sons, Ltd.

First flowering events in cherry trees are believed to be closely related to temperature patterns during the winter and spring months. Earlier works have incorporated the idea of temperature thresholds, defining chill and heat functions based on these thresholds. However, selection of the thresholds is often arbitrary and shared across species and locations. We propose a survival model with spatially and temporally varying covariates having functional forms representing chill and heat accumulation leading up to first flowering events. Thresholds are chosen utlizing the ranked probability scores, selecting the threshold pair that minimizes the difference between the predicted and observed cumulative probability curves. We first apply the model using temporally varying covariates to analyze 29 years of flowering data for four cherry species (Cerasus spp.) grown in Hachioji, Japan. This allows us to investigate whether relationship with temperature may vary between earlier and later flowering species. Next, the model is applied to 52 years of flowering data for 45 Cerasus spachiana × C. speciosa trees grown across Japan's Honshu Island using spatially and temporally varying covariates and spatial random effects. By exploring flowering dates across locations, we can explore how the relationship between temperature and first flowering events varies through space. Copyright © 2013 John Wiley & Sons, Ltd.

Understanding past relative sea-level changes is important to a number of social and scientific questions, including the effects of global climate change and future land-use planning under scenarios of accelerated sea-level rise with a concomitant increased threat to coastal areas around the world. In particular, accurately characterizing millennial sea-level changes is important in evaluating vertical movements of the Earth's crust that happen in response to the advances and retreats of ice sheets during long-term climatic cycles. In this paper, we analyze sea-level data from several Maine salt marshes previously reported in a paper from the geological literature. We address these data and questions of geological interest with a ‘smooth transition’ model. Copyright © 2013 John Wiley & Sons, Ltd.

In research into the main causes of recent global warming, the Granger causal link from anthropogenic forcings to global temperature has often been analyzed in a bivariate system, with an evidence of a causal effect of greenhouse gases on temperature as the final result. In the present paper, the robustness of these results are investigated by considering an auxiliary variable in the system. In particular, we include natural forcings and some patterns of natural variability, separately, as the third variable. Previous bivariate Granger results are shown to be robust. Copyright © 2013 John Wiley & Sons, Ltd.

Bayesian inference on the change points in a given sample is a statistical model selection problem that presents two main difficulties. The first one is the selection of a reasonable prior distribution over the set of models, the number of which depends exponentially on the sample size, and the second is the high computational burden involved even when Markov chain Monte Carlo methods are used.

Due to the increasing availability of sensors of moderate cost, large scale sensor networks are currently considered for different tasks. One of these is the monitoring of air quality with several, possibly hundreds of sensors that may cover an extensive area, or may be difficult to access. In this paper, we consider the detection of sensor malfunctions in sensor networks. A change in the statistics of a particular sensor can be either due to local variability or a malfunction, and thus simple change detection approaches do not necessarily apply. Here, we estimate the output of each sensor based on all the other sensors using approximate Wiener filters. The approach focuses on the variance of the related prediction errors and also facilitates the control of the trade-off between the probability of detecting a potential sensor malfunction and the related degree of errors. As a case study, we consider daily ozone data from the Houston air quality monitoring network. Copyright © 2013 John Wiley & Sons, Ltd.

Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers. Via simulation of Gaussian state space models, we are able to estimate aggregated yearly phosphorus and nitrogen fluxes, and their confidence intervals.

When the goal of a study is to compare two groups on an ordinal categorical scale, a large number of inferential methods are available. Most methods are designed to detect a location effect, such as by focusing a single-degree-of-freedom test on an effect parameter. Often, rather than merely summarizing by a P-value to describe the evidence against a null hypothesis, it is of interest to consider whether a stronger conclusion can be made. For example, can we conclude that the population distributions are stochastically ordered? For parameter space regions described by order restrictions, frequentist methods are not well designed for significance testing. For example, a frequentist P-value for testing identical distributions against an alternative of stochastically ordered distributions can be very small even when the sample distributions give clear evidence that the distributions do not have the ordering property. The Bayesian approach seems better equipped to handle such questions. We discuss this in the context of stochastic ordering and other types of ordinal odds ratio structure, for the two-group comparison and for more general contexts. For Dirichlet priors, simple simulations provide posterior probabilities of particular ordinal odds ratio structures. Copyright © 2013 John Wiley & Sons, Ltd.

An important objective in environmental risk assessment is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre-specified benchmark response in a dose–response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well-known concern, however, that existing parametric estimation techniques are sensitive to the form used for modeling the dose response. If the chosen parametric model is in fact misspecified, this can lead to inaccurate low-dose inferences. Indeed, avoiding the impact of model selection was one early motivating issue behind the development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating BMDs, on the basis of information-theoretic weights. We explore how the strategy can be used to build one-sided lower confidence limits on the BMD, and we study the confidence limits' small-sample properties via a simulation study. An example from environmental carcinogenicity testing illustrates the calculations. It is seen that application of this information-theoretic, model averaging methodology to benchmark analysis can improve environmental health planning and risk regulation when dealing with low-level exposures to hazardous agents. Copyright © 2013 John Wiley & Sons, Ltd.

When fitting a binomial geostatistical model to data obtained by spatially discrete sampling, techniques such as Markov chain Monte Carlo, which are computationally intensive and require careful tuning to each application, need to be employed. As a result, this approach is often infeasible when computational resources are scarce or statistical expertise is unavailable. A practical solution to this problem is to transform the binomial samples such that the conditional distribution of the transformed data can be assumed to be approximately Gaussian. Likelihood-based inference can then be performed analytically, whereas Bayesian inference requires only routine Monte Carlo simulation. In this paper, the predictive performance of the binomial model-fitting method is compared with that of the approximate transformed Gaussian method. A simulation experiment is undertaken in which data are simulated from a binomial geostatistical model, and both methods are then used to make predictive inference. Predictions are assessed using the empirical root mean square prediction error and empirical coverage probabilities of nominal prediction intervals obtained for the approximate method. As expected, the comparative performance of the approximate method deteriorates as the denominator decreases and, to a lesser extent, as the overall proportion of successes in the simulated data deviates from 0.5. Our results provide guidance on when the approximate method can safely be used. We demonstrate the two methods on village-level prevalence data pertaining to the tropical eye disease Loa loa. Copyright © 2013 John Wiley & Sons, Ltd.

Ordinary differential equation based models find application in a wide variety of biological and physiological phenomena. For instance, they arise in the description of gene regulatory networks, study of viral dynamics and other infectious diseases and so on. In the field of toxicology, they are used in physiologically based pharmacokinetic models for describing absorption, distribution, metabolism and excretion of a chemical in vivo. Knowledge about the model parameters is important in understanding the mechanism of action of a chemical and are often estimated using nonlinear least squares methodology. However, there are several challenges associated with the usual methodology. Using functional data analytic methodology, in this article, we develop a general framework for drawing inferences on parameters in models described by a system of differential equations. The proposed methodology takes into account variability between and within experimental units. The performance of the proposed methodology is evaluated using a simulation study and data obtained from a benzene inhalation study. We also describe an R-based software developed toward this purpose. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, generalized additive mixed models are constructed for the analysis of geographical and temporal variability of disease ratios. In this class of models, spatio–temporal models that use conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are considered. The frequentist analysis of these complex models is computationally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recently developed data cloning (DC) method provides a frequentist approach to mixed models and equally computationally convenient. We propose to use DC, which yields to maximum likelihood estimation, to conduct frequentist analysis of spatio–temporal modeling of disease ratios. The advantages of DC approach are that the non-estimable parameters are flagged automatically and prediction (and prediction intervals) of the smoothing incidence ratios over space and time are easily obtained. We illustrate this approach using a real dataset of yearly childhood asthma visits to hospital in the province of Manitoba, Canada, during 2000–2010. The performance of the DC approach is also studied through a simulation study. Copyright © 2013 John Wiley & Sons, Ltd.

With the proliferation of modern high-resolution measuring instruments mounted on satellites, planes, ground-based vehicles, and monitoring stations, a need has arisen for statistical methods suitable for the analysis of large spatial datasets observed on large spatial domains. Statistical analyses of such datasets provide two main challenges: first, traditional spatial-statistical techniques are often unable to handle large numbers of observations in a computationally feasible way; second, for large and heterogeneous spatial domains, it is often not appropriate to assume that a process of interest is stationary over the entire domain. We address the first challenge by using a model combining a low-rank component, which allows for flexible modeling of medium-to-long-range dependence via a set of spatial basis functions, with a tapered remainder component, which allows for modeling of local dependence using a compactly supported covariance function. Addressing the second challenge, we propose two extensions to this model that result in increased flexibility: first, the model is parameterized on the basis of a nonstationary Matérn covariance, where the parameters vary smoothly across space; second, in our fully Bayesian model, all components and parameters are considered random, including the number, locations, and shapes of the basis functions used in the low-rank component. Using simulated data and a real-world dataset of high-resolution soil measurements, we show that both extensions can result in substantial improvements over the current state-of-the-art. Copyright © 2013 John Wiley & Sons, Ltd.

In many environmental studies, the main focus is on observational economy, that is, to obtain data on the basis of cost-effective and efficient sampling methods. In this paper, we propose a partial ranked set sampling (PRSS) method for estimation of population mean, median and variance. On the basis of perfect and imperfect rankings, Monte Carlo simulations from symmetric and asymmetric distributions are used to evaluate the effectiveness of the proposed estimators. It is found that the estimators under PRSS are more efficient than the estimators based on simple random sampling. The procedure is illustrated with a case study using a real data set. Copyright © 2013 John Wiley & Sons, Ltd.