Environmetrics最新文献_第3页

P-min-Stable Regression Models for Time Series With Extreme Values of Limited Range

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2025-01-31 DOI: 10.1002/env.2897

Leonardo Brandao Freitas Nascimento, Max Sousa Lima, Luiz H. Duczmal

{"title":"P-min-Stable Regression Models for Time Series With Extreme Values of Limited Range","authors":"Leonardo Brandao Freitas Nascimento, Max Sousa Lima, Luiz H. Duczmal","doi":"10.1002/env.2897","DOIUrl":"https://doi.org/10.1002/env.2897","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, a <i>P-min-stable</i> regression model is proposed for a time series of extreme values observed in a limited interval. The model may be useful when the variable or indicator of interest is the minimum value of a series restricted to the unit interval and is related to other variables through a regression structure. The serial extremal dependence is induced through the marginalization of the Kumaraswamy distribution conditioned on a latent <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math>-stable process. The model is flexible to capture trends, seasonality, and non-stationarity. Some properties of the model are presented, as well as the extremogram of the series. Procedures for estimation and inference are discussed and implemented via an Expectation-Maximization algorithm. As an illustration, the model was used to analyze the minimum relative humidity observed in the Brazilian Amazon.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121397","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}

引用次数: 0

2024 Editorial Collaborators

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2025-01-22 DOI: 10.1002/env.2899

引用次数: 0

Modeling Anisotropy and Non-Stationarity Through Physics-Informed Spatial Regression

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-12-05 DOI: 10.1002/env.2889

Matteo Tomasetto, Eleonora Arnone, Laura M. Sangalli

{"title":"Modeling Anisotropy and Non-Stationarity Through Physics-Informed Spatial Regression","authors":"Matteo Tomasetto, Eleonora Arnone, Laura M. Sangalli","doi":"10.1002/env.2889","DOIUrl":"https://doi.org/10.1002/env.2889","url":null,"abstract":"<p>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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2889","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248623","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}

引用次数: 0

Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-12-05 DOI: 10.1002/env.2887

David Jobst, Annette Möller, Jürgen Groß

{"title":"Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas","authors":"David Jobst, Annette Möller, Jürgen Groß","doi":"10.1002/env.2887","DOIUrl":"https://doi.org/10.1002/env.2887","url":null,"abstract":"<p>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 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>τ</mi>\u0000 </mrow>\u0000 <annotation>$$ tau $$</annotation>\u0000 </semantics></math> 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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248624","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}

引用次数: 0

Modeling Disease Dynamics From Spatially Explicit Capture-Recapture Data

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-12-02 DOI: 10.1002/env.2888

Fabian R. Ketwaroo, Eleni Matechou, Matthew Silk, Richard Delahay

{"title":"Modeling Disease Dynamics From Spatially Explicit Capture-Recapture Data","authors":"Fabian R. Ketwaroo, Eleni Matechou, Matthew Silk, Richard Delahay","doi":"10.1002/env.2888","DOIUrl":"https://doi.org/10.1002/env.2888","url":null,"abstract":"<p>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 (<i>Meles meles</i>), naturally infected with bovine tuberculosis (<i>Mycobacterium bovis</i>, 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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110329","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}

引用次数: 0

Calibrating Satellite Maps With Field Data for Improved Predictions of Forest Biomass

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-11-28 DOI: 10.1002/env.2892

Paul B. May, Andrew O. Finley

{"title":"Calibrating Satellite Maps With Field Data for Improved Predictions of Forest Biomass","authors":"Paul B. May, Andrew O. Finley","doi":"10.1002/env.2892","DOIUrl":"https://doi.org/10.1002/env.2892","url":null,"abstract":"<div>\u0000 \u0000 <p>Spatially explicit quantification of forest biomass is important for forest-health monitoring and carbon accounting. Direct field measurements of biomass are laborious and expensive, typically limiting their spatial and temporal sampling density and therefore the precision and resolution of the resulting inference. Satellites can provide biomass predictions at a far greater density, but these predictions are often biased relative to field measurements and exhibit heterogeneous errors. We developed and implemented a coregionalization model between sparse field measurements and a predictive satellite map to deliver improved predictions of biomass density at a 1 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>km</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{km}}^2 $$</annotation>\u0000 </semantics></math> resolution throughout the Pacific states of California, Oregon and Washington. The model accounts for zero-inflation in the field measurements and the heterogeneous errors in the satellite predictions. A stochastic partial differential equation approach to spatial modeling is applied to handle the magnitude of the satellite data. The spatial detail rendered by the model is much finer than would be possible with the field measurements alone, and the model provides substantial noise-filtering and bias-correction to the satellite map.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120339","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}

引用次数: 0

A Varying Precision Beta Prime Autoregressive Moving Average Model With Application to Water Flow Data

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-11-25 DOI: 10.1002/env.2886

Kleber H. Santos, Francisco Cribari-Neto

{"title":"A Varying Precision Beta Prime Autoregressive Moving Average Model With Application to Water Flow Data","authors":"Kleber H. Santos, Francisco Cribari-Neto","doi":"10.1002/env.2886","DOIUrl":"https://doi.org/10.1002/env.2886","url":null,"abstract":"<div>\u0000 \u0000 <p>We introduce a dynamic model tailored for positively valued time series. It accommodates both autoregressive and moving average dynamics and allows for explanatory variables. The underlying assumption is that each random variable follows, conditional on the set of previous information, the beta prime distribution. A novel feature of the proposed model is that both the conditional mean and conditional precision evolve over time. The model thus comprises two dynamic submodels, one for each parameter. The proposed model for the conditional precision parameter is parsimonious, incorporating first-order time dependence. Changes over time in the shape of the density are determined by the time evolution of two parameters, and not just of the conditional mean. We present simple closed-form expressions for the model's conditional log-likelihood function, score vector, and Fisher's information matrix. Monte Carlo simulation results are presented. Finally, we use the proposed approach to model and forecast two seasonal water flow time series. Specifically, we model the inflow and outflow rates of the reservoirs of two hydroelectric power plants. Overall, the forecasts obtained using the proposed model are more accurate than those yielded by alternative models.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253408","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}

引用次数: 0

Characterizing Asymptotic Dependence between a Satellite Precipitation Product and Station Data in the Northern US Rocky Mountains via the Tail Dependence Regression Framework With a Gibbs Posterior Inference Approach

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-11-24 DOI: 10.1002/env.2890

Brook T. Russell, Yiren Ding, Whitney K. Huang, Jamie L. Dyer

{"title":"Characterizing Asymptotic Dependence between a Satellite Precipitation Product and Station Data in the Northern US Rocky Mountains via the Tail Dependence Regression Framework With a Gibbs Posterior Inference Approach","authors":"Brook T. Russell, Yiren Ding, Whitney K. Huang, Jamie L. Dyer","doi":"10.1002/env.2890","DOIUrl":"https://doi.org/10.1002/env.2890","url":null,"abstract":"<p>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.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253269","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}

引用次数: 0

Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-11-05 DOI: 10.1002/env.2884

Emrah Gecili, Cole Brokamp, Özgür Asar, Eleni-Rosalina Andrinopoulou, John J. Brewington, Rhonda D. Szczesniak

{"title":"Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression","authors":"Emrah Gecili, Cole Brokamp, Özgür Asar, Eleni-Rosalina Andrinopoulou, John J. Brewington, Rhonda D. Szczesniak","doi":"10.1002/env.2884","DOIUrl":"https://doi.org/10.1002/env.2884","url":null,"abstract":"<div>\u0000 \u0000 <p>Select measures of social and environmental determinants of health (referred to as “geomarkers”), predict rapid lung function decline in cystic fibrosis (CF), defined as a prolonged decline relative to patient and/or center-level norms. The extent to which hyper-localization, defined as increasing the spatiotemporal precision of geomarkers, aids in prediction of rapid lung decline remains unclear. Linear mixed effects (LME) models with specialized covariance functions have been used for predicting rapid lung function decline, but there are few options to properly incorporate spatial correlation into the covariance functions while inducing simultaneous variable selection. Our innovative Bayesian model uses a spike and slab prior for simultaneous variable selection and offers additional advantages when coupled with nonstationary Gaussian LME modeling. This model also incorporates spatial correlation through an additional random effect term that accounts for spatial correlation based on ZIP code distances. We validated the model with simulations and applied it to real CF data from a Midwestern CF Center. We demonstrate how a combination of demographic, clinical, and geomarker variables can be selected as optimal predictors using Bayesian false discovery rate controlling rule. Our results indicate that incorporating spatiotemporal effects and geomarkers into this novel Bayesian stochastic LME model enhances the dynamic prediction of rapid CF disease progression.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112253","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}

引用次数: 0

Entropy-Based Assessment of Biodiversity, With Application to Ants' Nests Data

IF 1.5 3区环境科学与生态学

Environmetrics Pub Date : 2024-10-30 DOI: 10.1002/env.2885

L. Altieri, D. Cocchi, M. Ventrucci

引用次数: 0