Environmetrics
Công bố khoa học tiêu biểu
* Dữ liệu chỉ mang tính chất tham khảo
Sắp xếp:
Bias correction in estimation of public health risk attributable to short‐term air pollution exposure Numerous epidemiologic studies have reported associations between short‐term air pollution exposure and mortality. Such short‐term risk models include smooth functions of time to control for unmeasured confounding variables. We demonstrate bias in these short‐term Generalized Additive Model estimates because of lack of accounting for long timescale variations and propose a family of improved time smoothers to reduce and control the bias. The strengths of the proposed smoother are twofold: a clear separating of short‐term and long‐term effects and an obvious choice of smoothing parameters from pre‐determined timescales of interest. We demonstrate improvements through simulations and analysis of examples of air pollution and mortality data from Chicago, Il. from the National Morbidity, Mortality and Air Pollution Study database, showing reduced bias in the risk estimates. © 2015 The Authors. Environmetrics Published by John Wiley & Sons Ltd.
Environmetrics - Tập 26 Số 4 - Trang 298-311 - 2015
HAC robust trend comparisons among climate series with possible level shifts Comparisons of trends across climatic data sets are complicated by the presence of serial correlation and possible step‐changes in the mean. We build on heteroskedasticity and autocorrelation robust methods, specifically the Vogelsang–Franses (VF) nonparametric testing approach, to allow for a step‐change in the mean (level shift) at a known or unknown date. The VF method provides a powerful multivariate trend estimator robust to unknown serial correlation up to but not including unit roots. We show that the critical values change when the level shift occurs at a known or unknown date. We derive an asymptotic approximation that can be used to simulate critical values, and we outline a simple bootstrap procedure that generates valid critical values and p ‐values. Our application builds on the literature comparing simulated and observed trends in the tropical lower troposphere and mid‐troposphere since 1958. The method identifies a shift in observations around 1977, coinciding with the Pacific Climate Shift. Allowing for a level shift causes apparently significant observed trends to become statistically insignificant. Model overestimation of warming is significant whether or not we account for a level shift, although null rejections are much stronger when the level shift is included. © 2014 The Authors. Environmetrics published by John Wiley & Sons, Ltd.
Environmetrics - Tập 25 Số 7 - Trang 528-547 - 2014
A parametric model for distributions with flexible behavior in both tails Summary For many problems of inference about a marginal distribution function, while the entire distribution is important, extreme quantiles are of particular interest because rare outcomes may have large consequences. In some applications, only the extreme upper quantiles require extra attention, but in, for example, climatological applications, extremes in both tails of the distribution can be impactful. A possible approach in this setting is to use parametric families of distributions that have flexible behavior in both tails. One way to quantify this property is to require that, for any two generalized Pareto distributions, there is a member of the parametric family that behaves like one of the generalized Pareto distributions in the upper tail and like the negative of the other generalized Pareto distribution in the lower tail. This work proposes some specific quantifications of this notion and describes parametric families of distributions that satisfy these specifications. The proposed families all have closed form expressions for their densities and, hence, are convenient for use in practice. A simulation study shows how one of the proposed families can work well for estimating all quantiles when both tails of a distribution are heavy tailed. An application to climate model output shows this family can also work well when applied to daily average January temperature near Calgary, for which the evolving distribution over time due to climate change is difficult to model accurately by any standard parametric family.
Environmetrics - Tập 32 Số 2 - 2021
Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels The share of wind energy in total installed power capacity has grown rapidly in recent years. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have the advantage of being able to produce spatially out‐of‐sample forecasts. We use a Bayesian hierarchical framework to obtain fast and accurate forecasts of wind power generation not only at wind farms where recent data are available but also at a larger portfolio including wind farms without recent observations of power production. The results and the methodologies are relevant for wind power forecasts across the globe and for spatiotemporal modeling in general.
Environmetrics - Tập 29 Số 3 - 2018
Different ways to compute temperature return levels in the climate change context Abstract The climate change context has raised new problems in the computation of temperature return levels (RLs) in using the statistical extreme value theory. This arises since it is not yet possible to accept the hypothesis that the series of maxima or of high level values are stationary, without at least verifying the assumption. Thus, in this paper, different approaches are tested and compared to derive high order RLs in the nonstationary context. These RLs are computed by extrapolating identified trends, and a bootstrap method is used to estimate confidence intervals. The identification of trends can be made either in the parameters of the extreme value distributions or in the mean and variance of the whole series. Then, a methodology is proposed to test if the trends in extremes can be explained by the trends in mean and variance of the whole dataset. If this is the case, the future extremes can be derived from the stationary extremes of the centered and normalized variable and the changes in mean and variance of the whole dataset. The RL can then be estimated as nonstationary or as stationary for fixed future periods. The work is done for both extreme value methods: block maxima and peak over threshold, and will be illustrated with the example of a long observation time series for daily maximum temperature in France. Copyright © 2010 John Wiley & Sons, Ltd.
Environmetrics - Tập 21 Số 7-8 - Trang 698-718 - 2010
Many zeros does not mean zero inflation: comparing the goodness-of-fit of parametric models to multivariate abundance data
Environmetrics - Tập 16 Số 3 - Trang 275-289 - 2005
Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error Current methods to correct for detection error require multiple visits to the same survey location. Many historical datasets exist that were collected using only a single visit, and logistical/cost considerations prevent many current research programs from collecting multiple visit data. In this paper, we explore what can be done with single visit count data when there is detection error. We show that when appropriate covariates that affect both detection and abundance are available, conditional likelihood can be used to estimate the regression parameters of a binomial–zero‐inflated Poisson (ZIP) mixture model and correct for detection error. We use observed counts of Ovenbirds (Seiurus aurocapilla ) to illustrate the estimation of the parameters for the binomial–zero‐inflated Poisson mixture model using a subset of data from one of the largest and longest ecological time series datasets that only has single visits. Our single visit method has the following characteristics: (i) it does not require the assumptions of a closed population or adjustments caused by movement or migration; (ii) it is cost effective, enabling ecologists to cover a larger geographical region than possible when having to return to sites; and (iii) its resultant estimators appear to be statistically and computationally highly efficient. Copyright © 2012 John Wiley & Sons, Ltd.
Environmetrics - Tập 23 Số 2 - Trang 197-205 - 2012
Spatial covariance modelling in a complex coastal domain by multidimensional scaling Abstract In aquatic studies, spatial interactions may be both easier to interpret and to quantify by using water distance than by using geographic distance. The water distance is the shortest path between those two sites that may be traversed entirely over water. One problem is that water distances may be non‐Euclidean, and thus covariance and variogram functions are not necessarily valid when using the water distance as a distance metric. Another problem is that the computation of water distances for a large set of spatial locations is computationally expensive. Our alternative is a computationally efficient method for calculation of a Euclidean approximation to water distances. The first step of the method is to define a triangular grid covering the complex domain of interest. Using this triangular grid, we pre‐compute approximate water distances using a graph search algorithm. These water distances are then approximated by multidimensional scaling, giving a Euclidean space. Finally, we use linear interpolation to move the data locations into the new Euclidean space. By using this method, subsequent computations of water distances between any locations can be done very fast and the method leads to a theoretically valid spatial covariance model. We apply our method to herring data from the Vestfjord system in Northern Norway. Copyright © 2003 John Wiley & Sons, Ltd.
Environmetrics - Tập 14 Số 3 - Trang 307-321 - 2003
Landscape‐based geostatistics: a case study of the distribution of blue crab in Chesapeake Bay Abstract Geostatistical techniques have gained widespread use in ecology and environmental science. Variograms are commonly used to describe and examine spatial autocorrelation, and kriging has become the method of choice for interpolating spatially‐autocorrelated variables. To date, most applications of geostatistics have defined the separation between sample points using simple Euclidean distance. In heterogeneous environments, however, certain landscape features may act as absolute or semi‐permeable barriers. This effective separation may be more accurately described by a measure of distance that accounts for the presence of barriers. Here we present an approach to geostatistics based on a lowest‐cost path (LCP) function, in which the cost of a path is a function of both the distance and the type of terrain crossed. The modified technique is applied to 13 years of survey data on blue crab abundance in Chesapeake Bay. Use of this landscape‐based distance metric significantly changed estimates of all three variogram parameters. In this case study, although local differences in kriging predictions were apparent, the use of the landscape‐based distance metric did not result in consistent improvements in kriging accuracy. Copyright © 2006 John Wiley & Sons, Ltd.
Environmetrics - Tập 17 Số 6 - Trang 605-621 - 2006
Comparative spatiotemporal analysis of fine particulate matter pollution Abstract The prime focus of this work is the comparative investigation, theoretical and numerical, of spatiotemporal techniques used in air pollution studies. Space‐time statistics techniques are classified on the basis of a set of criteria and the relative theoretical merits of each technique are discussed accordingly. The numerical comparison involves the applications of two representative techniques. For this purpose, the popular spatiotemporal epistemic knowledge synthesis and graphical user interface (SEKS‐GUI) software of spatiotemporal statistics is used together with a dataset of PM2.5 daily measurements obtained at monitoring stations geographically distributed over the state of North Carolina, USA. The analysis offers valuable insight concerning the choice of an appropriate spatiotemporal technique in air pollution studies. Copyright © 2009 John Wiley & Sons, Ltd.
Environmetrics - Tập 21 Số 3-4 - Trang 305-317 - 2010
Tổng số: 20
- 1
- 2