Environmetrics最新文献_第10页

Subordinated Gaussian processes for solar irradiance 太阳辐照度的隶属高斯过程

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-03-23 DOI: 10.1002/env.2800

Caitlin M. Berry, William Kleiber, Bri-Mathias Hodge

引用次数: 0

Multistage hierarchical capture–recapture models 多阶段分层捕获-重新捕获模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-03-20 DOI: 10.1002/env.2799

Mevin B. Hooten, Michael R. Schwob, Devin S. Johnson, Jacob S. Ivan

引用次数: 3

Approximation of Bayesian Hawkes process with inlabru 用inlabru逼近Bayesian-Hawkes过程

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-03-14 DOI: 10.1002/env.2798

Francesco Serafini, Finn Lindgren, Mark Naylor

{"title":"Approximation of Bayesian Hawkes process with inlabru","authors":"Francesco Serafini, Finn Lindgren, Mark Naylor","doi":"10.1002/env.2798","DOIUrl":"https://doi.org/10.1002/env.2798","url":null,"abstract":"Hawkes process are very popular mathematical tools for modeling phenomena exhibiting a self-exciting or self-correcting behavior. Typical examples are earthquakes occurrence, wild-fires, drought, capture-recapture, crime violence, trade exchange, and social network activity. The widespread use of Hawkes process in different fields calls for fast, reproducible, reliable, easy-to-code techniques to implement such models. We offer a technique to perform approximate Bayesian inference of Hawkes process parameters based on the use of the R-package inlabru . The inlabru R-package, in turn, relies on the INLA methodology to approximate the posterior of the parameters. Our Hawkes process approximation is based on a decomposition of the log-likelihood in three parts, which are linearly approximated separately. The linear approximation is performed with respect to the mode of the parameters' posterior distribution, which is determined with an iterative gradient-based method. The approximation of the posterior parameters is therefore deterministic, ensuring full reproducibility of the results. The proposed technique only requires the user to provide the functions to calculate the different parts of the decomposed likelihood, which are internally linearly approximated by the R-package inlabru . We provide a comparison with the bayesianETAS R-package which is based on an MCMC method. The two techniques provide similar results but our approach requires two to ten times less computational time to converge, depending on the amount of data.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50132691","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: 1

New estimation methods for extremal bivariate return curves 极值二元回归曲线的新估计方法

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-17 DOI: 10.1002/env.2797

C. J. R. Murphy-Barltrop, J. L. Wadsworth, E. F. Eastoe

引用次数: 2

Environmental data science: Part 2 环境数据科学：第2部分

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-16 DOI: 10.1002/env.2788

Wesley S. Burr, Nathaniel K. Newlands, Andrew Zammit-Mangion

引用次数: 0

Comparing emulation methods for a high-resolution storm surge model 高分辨率风暴潮模型仿真方法的比较

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-14 DOI: 10.1002/env.2796

Grant Hutchings, Bruno Sansó, James Gattiker, Devin Francom, Donatella Pasqualini

{"title":"Comparing emulation methods for a high-resolution storm surge model","authors":"Grant Hutchings, Bruno Sansó, James Gattiker, Devin Francom, Donatella Pasqualini","doi":"10.1002/env.2796","DOIUrl":"https://doi.org/10.1002/env.2796","url":null,"abstract":"Realistic simulations of complex systems are fundamental for climate and environmental studies. Large computer systems are often not sufficient to run sophisticated computational models for large numbers of different input settings. Statistical surrogate models, or emulators, are key tools enabling fast exploration of the simulator input space. Gaussian processes have become standard for computer simulator emulation. However, they require careful implementation to scale appropriately, motivating alternative methods more recently introduced. We present a comparison study of surrogates of the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) simulator—the simulator of choice for government agencies—using four emulation approaches: BASS; BART; SEPIA; and RobustGaSP. SEPIA and RobustGaSP use Gaussian processes, BASS implements adaptive splines, and BART is based on ensembles of regression trees. We describe the four models and compare them in terms of computation time and predictive metrics. These surrogates use proven and distinct methodologies, are available through accessible software, and quantify prediction uncertainty. Our data cover millions of response values. We find that SEPIA and RobustGaSP provide exceptional predictive power, but cannot scale to emulate experiments as large as the one considered in this paper as effectively as BASS and BART.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50132634","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: 4

Smooth copula-based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada 基于光滑copula的加拿大中东部极端降雨广义极值模型和空间插值

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-11 DOI: 10.1002/env.2795

Fatima Palacios-Rodriguez, Elena Di Bernardino, Melina Mailhot

引用次数: 2

A Bayesian change point modeling approach to identify local temperature changes related to urbanization 一种识别与城市化相关的局部温度变化的贝叶斯变点建模方法

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-11 DOI: 10.1002/env.2794

C. Berrett, B. Gurney, D. Arthur, T. Moon, G. P. Williams

{"title":"A Bayesian change point modeling approach to identify local temperature changes related to urbanization","authors":"C. Berrett, B. Gurney, D. Arthur, T. Moon, G. P. Williams","doi":"10.1002/env.2794","DOIUrl":"https://doi.org/10.1002/env.2794","url":null,"abstract":"Changes to the environment surrounding a temperature measuring station can cause local changes to the recorded temperature that deviate from regional temperature behavior. This phenomenon—often caused by construction or urbanization—occurs at a local level. If these local changes are assumed to represent regional or global processes it can have significant impacts on historical data analyses. These changes or deviations are generally gradual, but can be abrupt, and arise as construction or other environmental changes occur near a recording station. We propose a methodology to examine if changes in temperature behavior at a point in time exist at a local level at various locations in a region assuming that regional or global processes are correlated among nearby stations. Specifically, we propose a Bayesian change point model for spatio-temporally dependent data where we select the number of change points at each location using a “forward” selection process using deviance information criterion. We then fit the selected version of the model and examine the linear slopes across time to quantify the local changes in long-term temperature behavior. We show the utility of this model and method using both synthetic data and observed temperature measurements from eight stations in Utah consisting of daily temperature data for 60 years.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50149034","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: 1

CO2 emissions and growth: A bivariate bidimensional mean-variance random effects model 二氧化碳排放与增长：一个双变量二维均方差随机效应模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-11 DOI: 10.1002/env.2793

Antonello Maruotti, Pierfrancesco Alaimo Di Loro

{"title":"CO2 emissions and growth: A bivariate bidimensional mean-variance random effects model","authors":"Antonello Maruotti, Pierfrancesco Alaimo Di Loro","doi":"10.1002/env.2793","DOIUrl":"https://doi.org/10.1002/env.2793","url":null,"abstract":"We introduce a bivariate bidimensional mixed-effects regression model, motivated by the analysis of <math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>CO</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{CO}}_2 $$</annotation>\u0000 </semantics></math> emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50128681","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: 1

Nonlinear prediction of functional time series 函数时间序列的非线性预测

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-05 DOI: 10.1002/env.2792

Haixu Wang, Jiguo Cao

{"title":"Nonlinear prediction of functional time series","authors":"Haixu Wang, Jiguo Cao","doi":"10.1002/env.2792","DOIUrl":"https://doi.org/10.1002/env.2792","url":null,"abstract":"We propose a nonlinear prediction (NOP) method for functional time series. Conventional methods for functional time series are mainly based on functional principal component analysis or functional regression models. These approaches rely on the stationary or linear assumption of the functional time series. However, real data sets are often nonstationary, and the temporal dependence between trajectories cannot be captured by linear models. Conventional methods are also hard to analyze multivariate functional time series. To tackle these challenges, the NOP method employs a nonlinear mapping for functional data that can be directly applied to multivariate functions without any preprocessing step. The NOP method constructs feature space with forecast information, hence it provides a better ground for predicting future trajectories. The NOP method avoids calculating covariance functions and enables online estimation and prediction. We examine the finite sample performance of the NOP method with simulation studies that consider linear, nonlinear and nonstationary functional time series. The NOP method shows superior prediction performances in comparison with the conventional methods. Three real applications demonstrate the advantages of the NOP method model in predicting air quality, electricity price and mortality rate.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50121496","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: 3