Environmetrics最新文献

{"title":"Elastic functional changepoint detection of climate impacts from localized sources","authors":"J. Derek Tucker,&nbsp;Drew Yarger","doi":"10.1002/env.2826","DOIUrl":"10.1002/env.2826","url":null,"abstract":"<p>Detecting changepoints in functional data has become an important problem as interest in monitoring of climate phenomenon has increased, where the data is functional in nature. The observed data often contains both amplitude (<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>y</mi>\u0000 </mrow>\u0000 <annotation>$$ y $$</annotation>\u0000 </semantics></math>-axis) and phase (<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>x</mi>\u0000 </mrow>\u0000 <annotation>$$ x $$</annotation>\u0000 </semantics></math>-axis) variability. If not accounted for properly, true changepoints may be undetected, and the estimated underlying mean change functions will be incorrect. In this article, an elastic functional changepoint method is developed which properly accounts for these types of variability. The method can detect amplitude and phase changepoints which current methods in the literature do not, as they focus solely on the amplitude changepoint. This method can easily be implemented using the functions directly or can be computed via functional principal component analysis to ease the computational burden. We apply the method and its nonelastic competitors to both simulated data and observed data to show its efficiency in handling data with phase variation with both amplitude and phase changepoints. We use the method to evaluate potential changes in stratospheric temperature due to the eruption of Mt. Pinatubo in the Philippines in June 1991. Using an epidemic changepoint model, we find evidence of a increase in stratospheric temperature during a period that contains the immediate aftermath of Mt. Pinatubo, with most detected changepoints occurring in the tropics as expected.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135316341","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":"Joint species distribution modeling with competition for space","authors":"Juho Kettunen,&nbsp;Lauri Mehtätalo,&nbsp;Eeva-Stiina Tuittila,&nbsp;Aino Korrensalo,&nbsp;Jarno Vanhatalo","doi":"10.1002/env.2830","DOIUrl":"10.1002/env.2830","url":null,"abstract":"<p>Joint species distribution models (JSDM) are among the most important statistical tools in community ecology. However, existing JSDMs cannot model mutual exclusion between species. We tackle this deficiency in the context of modeling plant percentage cover data, where mutual exclusion arises from limited growing space and competition for light. We propose a hierarchical JSDM where latent Gaussian variable models describe species' niche preferences and Dirichlet-Multinomial distribution models the observation process and competition between species. We also propose a decision theoretic model comparison and validation approach to assess the goodness of JSDMs in four different types of predictive tasks. We apply our models and methods to a case study on modeling vegetation cover in a boreal peatland. Our results show that ignoring the interspecific interactions and competition reduces models' predictive performance and leads to biased estimates for total percentage cover. Models' relative predictive performance also depends on the predictive task highlighting that model comparison and assessment should resemble the true predictive task. Our results also demonstrate that the proposed JSDM can be used to simultaneously infer interspecific correlations in niche preference as well as mutual competition for space and through that provide novel insight into ecological research.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885170","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 spatially-weighted AMH copula-based dissimilarity measure for clustering variables: An application to urban thermal efficiency","authors":"F. Marta L. Di Lascio,&nbsp;Andrea Menapace,&nbsp;Roberta Pappadà","doi":"10.1002/env.2828","DOIUrl":"10.1002/env.2828","url":null,"abstract":"<p>Investigating thermal energy demand is crucial for developing sustainable cities and the efficient use of renewable sources. Despite the advances made in this field, the analysis of energy data provided by smart grids is currently a demanding challenge due to their complex multivariate structure and high dimensionality. In this article, we propose a novel copula-based dissimilarity measure suitable for analyzing district heating demand and introduce a procedure to apply it to high-temporal resolution panel data. Inspired by the characteristics of the considered data, we explore the usefulness of the Ali-Mikhail-Haq copula in defining a new dissimilarity measure to cluster variables in the hierarchical framework. We show that our proposal is particularly sensitive to small dissimilarities based on tiny differences in the strength of the dependence between the involved random variables. Therefore, the measure we introduce is able to distinguish between objects with low dissimilarity better than standard rank-based dissimilarity measures. Moreover, our proposal considers a weighted version of the copula-based dissimilarity that embeds the spatial location of the involved objects. We investigate the proposed measure through Monte Carlo studies and compare it with an analogous dissimilarity measure based on Kendall's correlation. Finally, the application to real data concerning the Italian city Bozen-Bolzano makes it possible to find clusters of buildings homogeneous with respect to their main characteristics, such as energy efficiency and heating surface. In turn, our findings may support the design, expansion, and management of district heating systems.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136034627","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":"Estimation of change with partially overlapping and spatially balanced samples","authors":"Xin Zhao,&nbsp;Anton Grafström","doi":"10.1002/env.2825","DOIUrl":"10.1002/env.2825","url":null,"abstract":"<p>Spatially balanced samples are samples that are well-spread in some available auxiliary variables. Selecting such samples has been proven to be very efficient in estimation of the current state (total or mean) of target variables related to the auxiliary variables. As time goes, or when new auxiliary variables become available, such samples need to be updated to stay well-spread and produce good estimates of the current state. In such an update, we want to keep some overlap between successive samples to improve the estimation of change. With this approach, we end up with partially overlapping and spatially balanced samples. To estimate the variance of an estimator of change, we need to be able to estimate the covariance between successive estimators of the current state. We introduce an approximate estimator of such covariance based on local means. By simulation studies, we show that the proposed estimator can reduce the bias compared to a commonly used estimator. Also, the new estimator tends to become less biased when reducing the local neighborhood size.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885296","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 Bayesian spatio-temporal model for short-term forecasting of precipitation fields","authors":"S. R. Johnson,&nbsp;S. E. Heaps,&nbsp;K. J. Wilson,&nbsp;D. J. Wilkinson","doi":"10.1002/env.2824","DOIUrl":"10.1002/env.2824","url":null,"abstract":"<p>With extreme weather events becoming more common, the risk posed by surface water flooding is ever increasing. In this work we propose a model, and associated Bayesian inference scheme, for generating short-term, probabilistic forecasts of localised precipitation on a spatial grid. Our generative hierarchical dynamic model is formulated in discrete space and time with a lattice-Markov spatio-temporal auto-regressive structure, inspired by continuous models of advection and diffusion. Observations from both weather radar and ground based rain gauges provide information from which we can learn the precipitation field through a latent process in addition to unknown model parameters. Working in the Bayesian paradigm provides a coherent framework for capturing uncertainty, both in the underlying model parameters and in our forecasts. Further, appealing to simulation based sampling using MCMC yields a straightforward solution to handling zeros, treated as censored observations, via data augmentation. Both the underlying state and the observations are of moderately large dimension (<math>\u0000 <mrow>\u0000 <mi>𝒪</mi>\u0000 <mo>(</mo>\u0000 <mn>1</mn>\u0000 <msup>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>4</mn>\u0000 </mrow>\u0000 </msup>\u0000 <mo>)</mo>\u0000 </mrow></math> and <math>\u0000 <mrow>\u0000 <mi>𝒪</mi>\u0000 <mo>(</mo>\u0000 <mn>1</mn>\u0000 <msup>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 <mo>)</mo>\u0000 </mrow></math> respectively) and this renders standard inference approaches computationally infeasible. Our solution is to embed the ensemble Kalman smoother within a Gibbs sampling scheme to facilitate approximate Bayesian inference in reasonable time. Both the methodology and the effectiveness of our posterior sampling scheme are demonstrated via simulation studies and by a case study of real data from the Urban Observatory project based in Newcastle upon Tyne, UK.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87367371","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":"Bayesian spatio-temporal survival analysis for all types of censoring with application to a wildlife disease study","authors":"Kehui Yao,&nbsp;Jun Zhu,&nbsp;Daniel J. O'Brien,&nbsp;Daniel Walsh","doi":"10.1002/env.2823","DOIUrl":"10.1002/env.2823","url":null,"abstract":"<p>In this article, we consider modeling arbitrarily censored survival data with spatio-temporal covariates. We demonstrate that under the piecewise constant hazard function, the likelihood for uncensored or right-censored subjects is proportional to the likelihood of multiple conditionally independent Poisson random variables. To address left- or interval-censored subjects, we propose to impute the exact event times and convert them into uncensored subjects, enabling the application of the integrated nested Laplace approximation to update model parameters using the imputed data. We introduce an iterative algorithm that alternates between imputing event times for left- and interval-censored subjects and re-estimating model parameters. The proposed method is assessed through a simulation study and applied to analyze a spatio-temporal survival dataset in a wildlife disease study investigating bovine tuberculosis in white-tailed deer in Michigan.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2823","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90708202","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":"Novel application of a process convolution approach for calibrating output from numerical models","authors":"Maike Holthuijzen,&nbsp;Dave Higdon,&nbsp;Brian Beckage,&nbsp;Patrick J. Clemins","doi":"10.1002/env.2822","DOIUrl":"10.1002/env.2822","url":null,"abstract":"<p>Output from numerical models at high spatial and temporal resolutions is critical for modeling applications in a variety of disciplines. Prior to its use in modeling, output from climate models must be brought to a finer spatial resolution and calibrated with respect to observations. The calibration of model output, referred to as bias-correction, poses many statistical challenges. Here, we develop a bias-correction method in which systematic biases in the mean and standard deviation of model output are corrected. In addition, we employ a novel process convolution approach to correct bias in temporal dependence. We apply this approach to temperature simulations generated by a regional climate model over the Northeastern USA. The goal of this study was to correct systematic bias in model simulations over historical (1976–2005) and future (2006–2099) time periods while simultaneously preserving future trends resulting from carbon emissions scenarios. We compare the proposed method to a quantile mapping method (empirical quantile mapping, EQM). The proposed method resulted in a more effective correction of seasonal biases and temporal dependence compared to EQM.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82801345","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":"Modeling temporally misaligned data across space: The case of total pollen concentration in Toronto","authors":"Sara Zapata-Marin,&nbsp;Alexandra M. Schmidt,&nbsp;Scott Weichenthal,&nbsp;Eric Lavigne","doi":"10.1002/env.2820","DOIUrl":"10.1002/env.2820","url":null,"abstract":"<p>Due to the high costs of monitoring environmental processes, measurements are commonly taken at different temporal scales. When observations are available at different temporal scales across different spatial locations, we name it temporal misalignment. Rather than aggregating the data and modeling it at the coarser scale, we propose a model that accounts simultaneously for the fine and coarser temporal scales. More specifically, we propose a spatiotemporal model that accounts for the temporal misalignment when one of the scales is the sum or average of the other by using the properties of the multivariate normal distribution. Inference is performed under the Bayesian framework, and uncertainty about unknown quantities is naturally accounted for. The proposed model is fitted to data simulated from different spatio-temporal structures to check if the proposed inference procedure recovers the true values of the parameters used to generate the data. The motivating example consists of measurements of total pollen concentration across Toronto, Canada. The data were recorded daily for some sites and weekly for others. The proposed model estimates the daily measurements at sites where only weekly data was recorded and shows how the temporal aggregation of the measurements affects the associations with different covariates.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76609119","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":"Bayesian functional emulation of CO2 emissions on future climate change scenarios","authors":"Luca Aiello,&nbsp;Matteo Fontana,&nbsp;Alessandra Guglielmi","doi":"10.1002/env.2821","DOIUrl":"10.1002/env.2821","url":null,"abstract":"<p>We propose a statistical emulator for a climate-economy deterministic integrated assessment model ensemble, based on a functional regression framework. Inference on the unknown parameters is carried out through a mixed effects hierarchical model using a fully Bayesian framework with a prior distribution on the vector of all parameters. We also suggest an autoregressive parameterization of the covariance matrix of the error, with matching marginal prior. In this way, we allow for a functional framework for the discretized output of the simulators that allows their time continuous evaluation.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84829917","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":"Air pollution estimation under air stagnation—A case study of Beijing","authors":"Ying Zhang,&nbsp;Song Xi Chen,&nbsp;Le Bao","doi":"10.1002/env.2819","DOIUrl":"https://doi.org/10.1002/env.2819","url":null,"abstract":"<p>Air pollution continues to be a major environmental concern in China. The wind-driven transmission poses difficulties in understanding the air pollution patterns at the local level. The main objective of this study is to offer a straightforward approach for investigating the temporal trends and meteorological effects on the air pollutant concentrations during the generation process without being confounded by the complex wind-driven transmission effect. We focus on the hourly data of the three most common air pollutants: PM2.5, NO<math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow></mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {}_2 $$</annotation>\u0000 </semantics></math>, and CO under air stagnation in Beijing, China, during 2014–2017. We find that the local pollution levels under air stagnation in Beijing have decreased over the years; winter is the severest month of the year; Sunday is the clearest day of the week. Our model also interpolates the air pollutant concentrations at sites without monitoring stations and provides a map of air pollution concentrations under air stagnation. The results could be used to identify locations where air pollutants easily accumulate.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50127885","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}