Jouni Helske  Jukka Nyblom  Petri Ekholm  Kristian Meissner 《Environmetrics》2013,24(4):237-247

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. The effect of model uncertainty is evaluated through a Monte Carlo experiment, where randomly selected sets of nutrient measurements are removed and then predicted by the remaining values together with re‐estimated parameters. Results show that our model performs well for rivers with long‐term records of flow. Finally, despite the drastic decreases in nutrient loads on the agricultural catchments of the rivers over the last 25 years, we observe no corresponding trends in riverine nutrient fluxes. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

    

Lauri Mehttalo  Timothy G. Gregoire  Harold E. Burkhart 《Environmetrics》2008,19(5):529-548

In many situations, including forest management planning, the underlying diameter distribution of a forest stand needs to be predicted. One alternative for predicting diameter distribution involves modeling diameter percentiles. We introduce a quantile regression (QR) approach for predicting diameter percentiles, and compare it with customary linear fixed‐effect and linear mixed‐effects models. The customary methods involve first estimating plot‐specific sample percentiles and then regressing them on stand characteristics, whereas QR directly models percentiles. We compared two prediction situations: a conditional one where a previously measured diameter sample is available from the stand of interest, and a marginal one where only some stand‐specific characteristics are known. To compare the predictions to the true underlying percentiles, we conducted a simulation study. The QR approach led to slight improvements in the marginal prediction situation. In the conditional situation, the mixed‐effect model led to clearly better predictions and should be preferred until QR methods have been developed for hierarchical data. It is extremely important to filter out the highly correlated sampling error from conditional predictions in order that the models predict underlying population percentiles rather than the realized sample percentiles. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

    

T. Sáfadi  D. Peña 《Environmetrics》2008,19(6):582-601

The Bayesian estimation of a dynamic factor model where the factors follow a multivariate autoregressive model is presented. We derive the posterior distributions for the parameters and the factors and use Monte Carlo methods to compute them. The model is applied to study the association between air pollution and mortality in the city of São Paulo, Brazil. Statistical analysis was performed through a Bayesian analysis of a dynamic factor model. The series considered were minimal temperature, relative humidity, air pollutant of PM₁₀ and CO, mortality circulatory disease and mortality respiratory disease. We found a strong association between air pollutant (PM₁₀), Humidity and mortality respiratory disease for the city of São Paulo. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

    

Ronaldo Dias  Nancy L. Garcia  Angelo Martarelli 《Environmetrics》2009,20(2):111-130

In this work we address the problem of estimating mean and covariance curves when the available sample consists of aggregated functional data. Consider a population divided into sub‐populations for which one wants to estimate the mean (typology) and covariance curves for each sub‐population. However, it is not possible (or too expensive) to obtain sample curves for single individuals. The available data are collective curves, sum of curves of different subsets of individuals belonging to the sub‐populations. We propose an estimation method based on B‐splines expansion. This method is consistent and simulation studies suggest that the proposed mean estimator is suitable even with very few replications. This problem was motivated by a real problem concerning the efficient distribution of electric energy in Southeast Brazil. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

    

Isabella Morlini 《Environmetrics》2007,18(8):823-840

When studying air pollution measurements at different sites in a spatial area, we may search for a typical pattern, common to all curves, describing the underlying air pollution process in a pre‐specified period. Another area of interest to support local authorities in air quality management may be the classification of the different sites in homogeneous clusters and the group ranking that follows. Yet, there is variation in both amplitude and dynamics among the air pollutant concentrations measured at the different monitoring stations. Analyzing such measurements, where the basic unit of information is the entire observed process rather than a string of numbers, involves finding the time shifts or the warping functions among curves. The analysis is much more complicated if we consider a multivariate process, that is, vector‐valued air pollutant measurements. Following our previous work where an improved dynamic time‐warping algorithm has been developed, especially in the multivariate case, and used both for classifying functional data and estimating the structural mean of a sample of curves, we analyzed the measurements of some air pollutants in Emilia Romagna (northern Italy). In addition, for the univariate analyses, we applied the self‐modeling warping function approach, which is also convenient for these data. Indeed, this method was found to be model‐free and enough flexible to capture very complex and highly non‐linear patterns. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

    

Dale L. Zimmerman  David M. Holland 《Environmetrics》2005,16(3):219-234

We consider the problem of optimal spatial prediction of an environmental variable using data from more than one sampling network. A model incorporating spatial dependence and measurement errors with network‐specific biases and variances serves as the basis for the analysis of the combined data from all networks. We develop the associated optimal prediction methodology, which we call complementary co‐kriging because (a) data from each network complements the other, and (b) the solutions to several prediction problems of interest are co‐kriging predictors. A hypothetical example illustrates how much better the complementary co‐kriging predictor can be, when compared to the ordinary kriging predictors from each network alone and to a ‘naive’ combined predictor. We use the methodology to obtain optimal predictions of wet nitrate concentration data over the eastern U.S. using data combined from the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) and the Clean Air Status and Trends Network (CASTNet). Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

    

Mahmoud Torabi 《Environmetrics》2014,25(5):341-350

The analysis of geographical and temporal variability of binomial data, using generalized additive mixed models, are considered. In this class of models, spatially correlated random effects and temporal components are adopted. The frequentist analysis of these complex models is computationally difficult. Recently developed method of data cloning has overcome the computational challenges of the analysis of mixed models from the frequentist approach. We use data cloning, which yields to maximum likelihood estimation, to propose frequentist analysis of spatiotemporal modeling of odds of disease. The advantages of the data cloning approach are that the prediction and prediction interval of the smoothing odds over space and time are easily obtained. We illustrate this approach using a real dataset of yearly asthma physician visits by children in the province of Manitoba, Canada, during 2000–2010. The performance of the proposed approach is also studied through a simulation study. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

    

M.J. &#x;Heaton  M. Katzfuss  C. Berrett  D.W. &#x;Nychka 《Environmetrics》2014,25(1):2-15

Remotely sensed data products are now routinely used to study various aspects of the Earth's atmosphere. These remote sensing datasets are typically very high dimensional, have near global coverage and exhibit nonstationary spatial correlation structures. Proper statistical analysis of these datasets should be sufficiently flexible to account for all these aspects. To this end, we develop a kernel convolution construction of spatial processes on a sphere. As is the case with kernel convolution constructions on the plane, we establish a link between stationary kernels and a stationary covariance function on the sphere via the spherical harmonic decomposition of the kernel. We also introduce the Kent distribution as an appropriate kernel with interpretable parameters to be used in the kernel convolution construction. We demonstrate the discrete kernel convolution model using a dataset of remotely sensed CO₂ concentrations over the globe. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

    

Daniela Cocchi  Carlo Trivisano 《Environmetrics》2002,13(2):139-153

The aim of this work is to check whether modifications in the length of the hunting seasons had an effect on the chance of reproduction of different species of ringed birds. We start from a national data set of ringing‐recovered data on three species of game birds. Only data on birds recovered as juveniles are used. Data on recoveries are organized in a 4‐way contingency table. Several generalized linear models are proposed for the counts of recovered birds. Bayesian hierarchical modeling is particularly suitable for this kind of data, for which an over‐dispersion parameter can be introduced at the second level of the hierarchy. Maximum Likelihood and Bayesian solutions are computed for the different models: the Bayesian framework, in particular under an individual modeling of over‐dispersion, exhibits the best fit in terms of Bayesian p‐value. The results show that the modification in the length of the hunting seasons does not produce equal benefits for the three species considered. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

    

Julien Damon  Serge Guillas 《Environmetrics》2002,13(7):759-774

In this article, we propose a new technique for ozone forecasting. The approach is functional, that is we consider stochastic processes with values in function spaces. We make use of the essential characteristic of this type of phenomenon by taking into account theoretically and practically the continuous time evolution of pollution. One main methodological enhancement of this article is the incorporation of exogenous variables (wind speed and temperature) in those models. The application is carried out on a six‐year data set of hourly ozone concentrations and meteorological measurements from Béthune (France). The study examines the summer periods because of the higher values observed. We explain the non‐parametric estimation procedure for autoregressive Hilbertian models with or without exogenous variables (considering two alternative versions in this case) as well as for the functional kernel model. The comparison of all the latter models is based on up‐to‐24 hour‐ahead predictions of hourly ozone concentrations. We analyzed daily forecast curves upon several criteria of two kinds: functional ones, and aggregated ones where attention is put on the daily maximum. It appears that autoregressive Hilbertian models with exogenous variables show the best predictive power. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献