Environmetrics最新文献

{"title":"Causal Discovery in Multivariate Extremes: A Study of Swiss Hydrological Catchments","authors":"L. Mhalla,&nbsp;V. Chavez-Demoulin,&nbsp;P. Naveau","doi":"10.1002/env.70034","DOIUrl":"https://doi.org/10.1002/env.70034","url":null,"abstract":"<p>Causally-induced asymmetry reflects the principle that an event qualifies as a cause only if its absence would prevent the occurrence of the effect. Thus, uncovering causal effects becomes a matter of comparing a well-defined score in both directions. Motivated by studying causal effects at extreme levels of a multivariate random vector, we propose to construct a model-agnostic causal score relying solely on the assumption of the existence of a max-domain of attraction. Based on a representation of a generalised Pareto random vector, we construct the causal score as the Wasserstein distance between the margins and a well-specified random variable. The proposed methodology is illustrated on a simulated dataset of different characteristics of catchments in Switzerland: discharge, precipitation, snowmelt, temperature, and evapotranspiration.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Extreme Wave Surges in the Presence of Missing Data","authors":"James H. McVittie,&nbsp;Orla A. Murphy","doi":"10.1002/env.70036","DOIUrl":"https://doi.org/10.1002/env.70036","url":null,"abstract":"<p>The block maxima approach, which consists of dividing a series of observations into equal-sized blocks to extract the block maxima, is commonly used for identifying and modeling extreme events using the generalized extreme value (GEV) distribution. In the analysis of coastal wave surge levels, the underlying data that generate the block maxima typically have missing observations. Consequently, the observed block maxima may not correspond to the true block maxima, yielding biased estimates of the GEV distribution parameters. Various parametric modeling procedures are proposed to account for the presence of missing observations under a block maxima framework. The performance of these estimators is compared through an extensive simulation study and illustrated by an analysis of extreme wave surges in Atlantic Canada.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined Quantile Forecasting for High-Dimensional Non-Gaussian Data","authors":"Seeun Park,&nbsp;Hee-Seok Oh,&nbsp;Yaeji Lim","doi":"10.1002/env.70035","DOIUrl":"https://doi.org/10.1002/env.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes a novel method for forecasting a scalar variable based on high-dimensional predictors that is applicable to various data distributions. In the literature, one of the popular approaches for forecasting with many predictors is to use factor models. However, these traditional methods are ineffective when the data exhibit non-Gaussian characteristics such as skewness or heavy tails. In this study, we newly utilize a quantile factor model to extract quantile factors that describe specific quantiles of the data beyond the mean factor. We then build a quantile-based forecast model using the estimated quantile factors at different quantile levels as predictors. Finally, the predicted values at various quantile levels are combined into a single forecast as a weighted average with weights determined by a Markov chain based on past trends of the target variable. The main idea of the proposed method is to effectively incorporate a quantile approach into a forecasting method to handle non-Gaussian characteristics. The performance of the proposed method is evaluated through a simulation study and real data analysis of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>PM</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>.</mo>\u0000 <mn>5</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{PM}}_{2.5} $$</annotation>\u0000 </semantics></math> data in South Korea, where the proposed method outperforms other existing methods in most cases.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multivariate Space-Time Dynamic Model for Characterizing the Atmospheric Impacts Following the Mt. Pinatubo Eruption","authors":"Robert C. Garrett,&nbsp;Lyndsay Shand,&nbsp;Gabriel Huerta","doi":"10.1002/env.70030","DOIUrl":"https://doi.org/10.1002/env.70030","url":null,"abstract":"<p>The June 1991 Mt. Pinatubo eruption resulted in a massive increase of sulfate aerosols in the atmosphere, absorbing radiation and leading to global changes in surface and stratospheric temperatures. A volcanic eruption of this magnitude serves as a natural analog for stratospheric aerosol injection, a proposed solar radiation modification method to combat a warming climate. The impacts of such an event are multifaceted and region-specific. Our goal is to characterize the multivariate and dynamic nature of the atmospheric impacts following the Mt. Pinatubo eruption. We developed a multivariate space-time dynamic linear model to understand the full extent of the spatially- and temporally-varying impacts. Specifically, spatial variation is modeled using a flexible set of basis functions for which the basis coefficients are allowed to vary in time through a vector autoregressive (VAR) structure. This novel model is cast in a Dynamic Linear Model (DLM) framework and estimated via a customized MCMC approach. We demonstrate how the model quantifies the relationships between key atmospheric parameters prior to and following the Mt. Pinatubo eruption with reanalysis data from MERRA-2 and highlight when such a model is advantageous over univariate models.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Spatial Hierarchical PGEV Model With Temporal Effects for Enhancing Extreme Value Analysis","authors":"Tzu-Han Peng,&nbsp;Cheng-Ching Lin,&nbsp;Nan-Jung Hsu,&nbsp;Chun-Shu Chen","doi":"10.1002/env.70031","DOIUrl":"https://doi.org/10.1002/env.70031","url":null,"abstract":"<div>\u0000 \u0000 <p>The peaks over threshold generalized extreme value (PGEV) model by Olafsdottir et al. (2021) is a statistical framework that combines the generalized extreme value (GEV) distribution with the peaks over threshold (PoT) approach, commonly utilized in extreme value analysis. This model effectively fits block maximum data, allowing for the estimation of trends in their intensity and frequency. Incorporating spatial and temporal effects into the PGEV model is crucial when analyzing climate and environmental datasets. We propose a novel spatial hierarchical PGEV model with temporal effects that captures spatial information via a latent Gaussian process applied to the PGEV parameters and integrates time covariates to account for temporal effects. To enhance computational efficiency, we employ the Laplace approximation method as an effective alternative to the traditional Markov Chain Monte Carlo (MCMC) parameter estimation techniques. We demonstrate the efficacy of our proposed methodology through extensive simulation studies covering various scenarios. Additionally, we illustrate the practical application of our model by analyzing rainfall data from Taiwan. Our findings highlight the model's potential for robust extreme value analysis in the context of climate research.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing Inter-Hemispheric Climate Change Asymmetries With a Cointegrated Vector Autoregression","authors":"Graziano Moramarco","doi":"10.1002/env.70026","DOIUrl":"https://doi.org/10.1002/env.70026","url":null,"abstract":"<p>We study the heterogeneity in climate change patterns between hemispheres using a cointegrated vector autoregression (CVAR) derived from an energy balance model. We provide new estimates of the responses of hemispheric climate conditions to shocks in radiative forcing, indicating stronger responses of surface temperature in the Northern than in the Southern Hemisphere, and similar responses of ocean heat content. The difference in equilibrium climate sensitivity between hemispheres is estimated to be around 1.2°C and statistically significant. We also use the model to make projections of the inter-hemispheric difference in temperature anomalies, conditional on the scenarios of forcing considered by the Intergovernmental Panel on Climate Change. The projections range from 0.5°C to 2.1°C in 2100, depending on the scenario. Stochastic forecasts based on the estimated CVAR model are used to assess the probability of alternative scenarios. Possible economic implications of asymmetries are discussed.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Many Eggs Are Too Many? Utilizing an Under-Dispersed Count Data Model to Gain Insights Into Evolutionary Productivity Constraints on Bird Species","authors":"James A. Clarke,&nbsp;Jeremy A. Smith,&nbsp;Ellie Leech,&nbsp;Philipp H. Boersch-Supan,&nbsp;Robert A. Robinson","doi":"10.1002/env.70032","DOIUrl":"https://doi.org/10.1002/env.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>Changes in productivity are primary mechanisms via which bird populations change and understanding how these processes operate is key to monitoring their populations in a changing environment. A major component of productivity is fecundity, the number of propagules produced, which for birds is the number of eggs laid (clutch size) and chicks that hatch from these (brood size). There are evolutionary constraints on the size of these fecundity measures and, therefore, variation tends to be smaller than other forms of count data. Using data on clutch and brood sizes for 55 and 52 UK bird species respectively we show these are consistently under-dispersed with respect to the standard Poisson model, which is often used to fit such data. A three-parameter exponentially weighted Poisson (EWP₃) model fits substantively better than either a Poisson or under-dispersed variants. We provide an R package to enable easy fitting of such models. The EWP₃ is characterized by two dispersion parameters, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>β</mi>\u0000 <mn>1</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {beta}_1 $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>β</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {beta}_2 $$</annotation>\u0000 </semantics></math>, and we suggest that these can quantify evolutionary constraints on incubation. We show that <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>β</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {beta}_2 $$</annotation>\u0000 </semantics></math> is generally greater than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>β</mi>\u0000 <mn>1</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {beta}_1 $$</annotation>\u0000 </semantics></math>, indicating a greater compression at the right hand end of the distribution. This suggests that the cost of having an extra egg or chick is higher than the cost of having one too few. Although we consider avian reproduction this method should be suitable for any species which has a small number of offspring in each reproductive event.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semiparametric Approaches for Mitigating Spatial Confounding in Large Environmental Epidemiology Cohort Studies","authors":"Maddie J. Rainey,&nbsp;Kayleigh P. Keller","doi":"10.1002/env.70028","DOIUrl":"https://doi.org/10.1002/env.70028","url":null,"abstract":"<p>Epidemiological analyses of environmental risk factors often include spatially varying exposures and outcomes. Unmeasured, spatially varying factors can lead to confounding bias in estimates of associations with adverse health outcomes. Several approaches for mitigating this bias have been developed using semiparametric splines. These methods use thin plate regression splines to account for the spatial variation present in the analysis but differ in how to select the amount of spatial smoothing and in whether the exposure, the outcome, or both are smoothed. We directly compare current approaches based on information criteria and cross-validation metrics and additionally introduce a hybrid method to selection that combines features from multiple existing approaches. We compare these methods in a simulation study to make a recommendation for the best approach for different settings and demonstrate their use in a study of environmental exposures on birth weight in a Colorado cohort.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Partially Varying-Coefficient Model With Skew-T Random Errors for Environmental Data Modeling","authors":"Christian Caamaño-Carrillo,&nbsp;Germán Ibacache-Pulgar,&nbsp;Bladimir Morales","doi":"10.1002/env.70029","DOIUrl":"https://doi.org/10.1002/env.70029","url":null,"abstract":"<div>\u0000 \u0000 <p>Partially varying-coefficient models (PVCMs) are an important tool in the modeling of environmental, economic, biomedical and other data, which have a parametric and a nonparametric component in their formulation. In addition to presenting interaction of the unknown smooth functions, which makes the classic linear regression models more flexible, such that generalizes to generalized additive models (GAMs) and models with varying coefficients (VCMs), which usually have a Gaussian distribution. In many cases the data tend to be more complex in the sense that they can present high levels of skewness and kurtosis. This article extends the version Gaussian PVCMs, allowing errors to present asymmetry and heavy tails, increasing the flexibility of this type of models where the Gaussian version remains a special case within this extended version. Specifically, the EM algorithm was developed for the estimation of parameters and development of diagnostic analysis through local influence. To evaluate the efficiency of the estimation, a simulation study was carried out. Finally, the model was applied to the datasets of the National Air Quality Information System (SINCA) of Chile, specifically to data of the Metropolitan Region of Santiago, considering as the study variable the particulate matter <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>PM</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>.</mo>\u0000 <mn>5</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{PM}}_{2.5} $$</annotation>\u0000 </semantics></math>, for the importance it represents in environmental pollution and population health issues.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Animal Trajectory Imputation and Uncertainty Quantification via Deep Learning","authors":"Kehui Yao,&nbsp;Ian P. McGahan,&nbsp;Jun Zhu,&nbsp;Daniel J. Storm,&nbsp;Daniel P. Walsh","doi":"10.1002/env.70027","DOIUrl":"https://doi.org/10.1002/env.70027","url":null,"abstract":"<p>Imputing missing data in animal trajectories is crucial for understanding animal movements during unobserved periods. However, the traditional methods, such as linear interpolation and the continuous-time correlated random walk model, are often inadequate to capture the complexity of animal movements. Here, we develop a deep learning approach to animal trajectory imputation by a conditional diffusion model. Unlike the traditional methods, our deep learning method uses observed data and external covariates to impute missing positions along an animal trajectory, capturing periodic patterns and the influence of covariates, which leads to more accurate imputations. In a case study of imputing deer trajectories, our method not only provides more accurate deterministic imputations than existing approaches but also achieves uncertainty quantification through probabilistic imputation.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}