Environmetrics最新文献

{"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}

{"title":"Occupancy Modeling for Rare Species Using Large Datasets: A Subsampling Approach","authors":"Johanna de Haan-Ward,&nbsp;Simon J. Bonner,&nbsp;Douglas G. Woolford","doi":"10.1002/env.70023","DOIUrl":"https://doi.org/10.1002/env.70023","url":null,"abstract":"<p>Citizen science monitoring programs, such as the Breeding Bird Survey, provide a wealth of data for understanding species abundance and distribution. However, traditional approaches for occupancy modeling of rare species can be difficult to apply to large, imbalanced datasets. We propose a new method for occupancy modeling where the original dataset is subsampled seasonally, keeping all sites with at least one detection along with a random sample of sites with no detections. Occupancy models cannot be fit directly to these subsampled data because the assumption of binomial sampling no longer holds. However, we show that the occupancy probability is adjusted by an offset, meaning inference on the effects of predictors is still valid. We propose a method for model fitting via direct maximum likelihood and demonstrate via simulation that this leads to computational gains. We illustrate our method using data on Canada Warblers (<i>Cardellina canadensis</i>) from the Breeding Bird Survey in Ontario, Canada from 1997 to 2018, where 95% of sites have zero detections annually, demonstrating that we can accurately estimate the occupancy and detection parameters, including estimating the effects of habitat covariates, using just 10% of the original dataset.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589960","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":"Estimation of Impact Ranges for Functional Valued Predictors","authors":"Rory Samuels,&nbsp;Nimrod Carmon,&nbsp;Bledar Komoni,&nbsp;Jonathan Hobbs,&nbsp;Amy Braverman,&nbsp;Dean Young,&nbsp;Joon Jin Song","doi":"10.1002/env.70024","DOIUrl":"https://doi.org/10.1002/env.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>Spectroscopy plays a crucial role in various scientific and industrial applications, enabling the analysis of complex materials and their interactions with incident radiation. Hyperspectral remote sensing, also known as imaging spectroscopy, is essential for numerous Earth science applications, spanning multiple disciplines, including ecology, geology, and cryosphere research. With the abundance of current orbital imaging spectrometers, and with space agencies and commercial companies set to expand their use in the next few years, developing methodologies that maximize the utility of these data is crucial. Identifying the wavelength ranges of diagnostic absorption features in spectra is essential for understanding the relationship between spectral data and responses of interest. In this paper, we propose a statistical approach that utilizes Functional Partial Least Squares (FPLS) to model the spectral data as smooth functions and study their impact on the response variable along sub-intervals of the domain. To capture the localized relationships within specific sub-intervals, termed impact ranges, we present a novel two-stage estimation procedure to identify the midpoint and half-length of the impact ranges. Additionally, we introduce an algorithm for iteratively applying the proposed two-stage approach to estimate both the number and location of potential impact ranges. The proposed procedure is evaluated via Monte Carlo simulation and is applied to a real dataset of spectra to identify the location of the diagnostic absorption features for predicting calcium carbonate (CaCO₃) content in soil. Our methodology accurately estimates the number and location of impact ranges, corresponding to absorption features in spectral data.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573662","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":"Computational Benchmark Study in Spatio-Temporal Statistics With a Hands-On Guide to Optimise R","authors":"Lorenzo Tedesco,&nbsp;Jacopo Rodeschini,&nbsp;Philipp Otto","doi":"10.1002/env.70017","DOIUrl":"https://doi.org/10.1002/env.70017","url":null,"abstract":"<p>This study provides a comprehensive evaluation of the computational performance of <span>R</span>, <span>MATLAB</span>, <span>Python</span>, and <span>Julia</span> for spatial and spatio-temporal modelling, focusing on high-dimensional datasets typical in geospatial statistical analysis. We benchmark each language across key tasks, including matrix manipulations and transformations, iterative programming routines, and Input/Output processes, all of which are critical in environmetrics. The results demonstrate that <span>MATLAB</span> excels in matrix-based computations, while <span>Julia</span> consistently delivers competitive performance across a wide range of tasks, establishing itself as a robust, open-source alternative. <span>Python</span>, when combined with libraries like <span>NumPy</span>, shows strength in specific numerical operations, offering versatility for general-purpose programming. <span>R</span>, despite its slower default performance in raw computations, proves to be highly adaptable; by linking to optimized libraries like <span>OpenBLAS</span> or <span>MKL</span> and integrating <span>C++</span> with packages like <span>Rcpp</span>, <span>R</span> achieves significant performance gains, becoming competitive with the other languages. This study also provides practical guidance for researchers to optimize <span>R</span> for geospatial data processing, offering insights to support the selection of the most suitable language for specific modelling requirements.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511146","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":"Does Wind Affect the Orientation of Vegetation Stripes? A Copula-Based Mixture Model for Axial and Circular Data","authors":"Marco Mingione,&nbsp;Francesco Lagona,&nbsp;Priyanka Nagar,&nbsp;Francois von Holtzhausen,&nbsp;Andriette Bekker,&nbsp;Janine Schoombie,&nbsp;Peter C. le Roux","doi":"10.1002/env.70021","DOIUrl":"https://doi.org/10.1002/env.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>Motivated by a case study of vegetation patterns, we introduce a mixture model with concomitant variables to examine the association between the orientation of vegetation stripes and wind direction. The proposal relies on a novel copula-based bivariate distribution for mixed axial and circular observations and provides a parsimonious and computationally tractable approach to examine the dependence of two environmental variables observed in a complex manifold. The findings suggest that dominant winds shape the orientation of vegetation stripes through a mechanism of neighboring plants providing wind shelter to downwind individuals.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367408","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}