Environmetrics最新文献

Many spatially dependent phenomena that are of interest in environmental problems are characterized by strong anisotropy and non-stationarity. Moreover, the data are often observed over regions with complex conformations, such as water bodies with complicated shorelines or regions with complex orography. Furthermore, the distribution of the data locations may be strongly inhomogeneous over space. These issues may challenge popular approaches to spatial data analysis. In this work, we show how we can accurately address these issues by spatial regression with differential regularization. We model the spatial variation by a Partial Differential Equation (PDE), defined upon the considered spatial domain. This PDE may depend upon some unknown parameters that we estimate from the data through an appropriate profiling estimation approach. The PDE may encode some available problem-specific information on the considered phenomenon, and permit a rich modeling of anisotropy and non-stationarity. The performances of the proposed approach are compared to competing methods through simulation studies and real data applications. In particular, we analyze rainfall data over Switzerland, characterized by strong anisotropy, and oceanographic data in the Gulf of Mexico, characterized by non-stationarity due to the Gulf Stream.

Vine copulas are flexible dependence models using bivariate copulas as building blocks. If the parameters of the bivariate copulas in the vine copula depend on covariates, one obtains a conditional vine copula. We propose an extension for the estimation of continuous conditional vine copulas, where the parameters of continuous conditional bivariate copulas are estimated sequentially and separately via gradient-boosting. For this purpose, we link covariates via generalized linear models (GLMs) to Kendall's $��\tau �$ correlation coefficient from which the corresponding copula parameter can be obtained. In a second step, an additional covariate deselection procedure is applied. The performance of the gradient-boosted conditional vine copulas is illustrated in a simulation study. Linear covariate effects in low- and high-dimensional settings are investigated separately for the conditional bivariate copulas and the conditional vine copulas. Moreover, the gradient-boosted conditional vine copulas are applied to the multivariate postprocessing of ensemble weather forecasts in a low-dimensional covariate setting. The results show that our suggested method is able to outperform the benchmark methods and identifies temporal correlations better. Additionally, we provide an R-package called boostCopula for this method.

One of the main aims of wildlife disease ecology is to identify how disease dynamics vary in space and time and as a function of population density. However, monitoring spatiotemporal and density-dependent disease dynamics in the wild is challenging because the observation process is error-prone, which means that individuals, their disease status, and their spatial locations are unobservable, or only imperfectly observed. In this paper, we develop a novel spatially-explicit capture-recapture (SCR) model motivated by an SCR data set on European badgers (Meles meles), naturally infected with bovine tuberculosis (Mycobacterium bovis, TB). Our model accounts for the observation process of individuals as a function of their latent activity centers, and for their imperfectly observed disease status and its effect on demographic rates and behavior. This framework has the advantage of simultaneously modeling population demographics and disease dynamics within a spatial context. It can therefore generate estimates of critical parameters such as population size; local and global density by disease status and hence spatially-explicit disease prevalence; disease transmission probabilities as functions of local or global population density; and demographic rates as functions of disease status. Our findings suggest that infected badgers have lower survival probability but larger home range areas than uninfected badgers, and that the data do not provide strong evidence that density has a non-zero effect on disease transmission. We also present a simulation study, considering different scenarios of disease transmission within the population, and our findings highlight the importance of accounting for spatial variation in disease transmission and individual disease status when these affect demographic rates. Collectively these results show our new model enables a better understanding of how wildlife disease dynamics are linked to population demographics within a spatiotemporal context.

The use of satellite precipitation products (SPP) allows for precipitation information to be collected nearly globally, but questions remain regarding their ability to reproduce extreme precipitation over mountainous terrain. In this work, we assess the ability of the precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR) to capture daily precipitation extremes by comparing PERSIANN-CDR with corresponding station data in the summer at remote locations in the northern US Rocky Mountains of Wyoming, Idaho, and Montana. The assessment utilizes the regular variation framework from extreme value theory and consists of two parts: (1) evaluating the extent to which PERSIANN-CDR can capture precipitation extremes through inference on an asymptotic dependence parameter, concluding that the level of asymptotic dependence is moderate throughout the region; (2) developing a tail dependence regression modeling framework and a Gibbs posterior approach for inference to investigate the degree to which elevation and topographic heterogeneity impact the level of asymptotic dependence, finding that the inclusion of a set of meteorological covariates, when combined with the PERSIANN-CDR output, yields an increased level of asymptotic dependence with station data.

The present work takes an innovative point of view in the study of a marked point pattern dataset of two ants' species, over an irregular region with a spatial covariate. The approach, based on entropy measures, brings new insights to the interpretation of the behavior of such ants' nesting habits, which can be exploited in the general area of biodiversity evaluation. We make proper use of descriptive entropy measures and inferential approaches, performing a comparative study of their uncertainty and interpretability in the context of biodiversity. For the first time in the study of these ants' nests data, all the available information is fully exploited, and interpretation guidelines are given for assessing both the observed and the latent biodiversity of the system, with a simultaneous consideration of spatial structures, covariate and interpoint interaction effects. Computations are supported by the new release of our R package SpatEntropy.

The hierarchical model-based (HMB) statistical method is currently applied in connection with NASA's Global Ecosystem Dynamics Investigation (GEDI) mission for assessing forest aboveground biomass (AGB) in areas lacking a sufficiently large number of GEDI footprints for employing hybrid inference. This study focuses on variance estimation using a bootstrap procedure that separates the computations into parts, thus considerably reducing the computational time required and making bootstrapping a viable option in this context. The procedure we propose uses a theoretical decomposition of the HMB variance into two parts. Through this decomposition, each variance component can be estimated separately and simultaneously. For demonstrating the proposed procedure, we applied a square-root-transformed ordinary least squares (OLS) model, and parametric bootstrapping, in the first modeling step of HMB. In the second step, we applied a random forest model and pairwise bootstrapping. Monte Carlo simulations showed that the proposed variance estimator is approximately unbiased. The study was performed on an artificial copula-generated population that mimics forest conditions in Oregon, USA, using a dataset comprising AGB, GEDI, and Landsat variables.