Environmetrics最新文献

{"title":"EM algorithm for generalized Ridge regression with spatial covariates","authors":"Said Obakrim,&nbsp;Pierre Ailliot,&nbsp;Valérie Monbet,&nbsp;Nicolas Raillard","doi":"10.1002/env.2871","DOIUrl":"10.1002/env.2871","url":null,"abstract":"<p>The generalized Ridge penalty is a powerful tool for dealing with multicollinearity and high-dimensionality in regression problems. The generalized Ridge regression can be derived as the mean of a posterior distribution with a Normal prior and a given covariance matrix. The covariance matrix controls the structure of the coefficients, which depends on the particular application. For example, it is appropriate to assume that the coefficients have a spatial structure when the covariates are spatially correlated. This study proposes an Expectation-Maximization algorithm for estimating generalized Ridge parameters whose covariance structure depends on specific parameters. We focus on three cases: diagonal (when the covariance matrix is diagonal with constant elements), Matérn, and conditional autoregressive covariances. A simulation study is conducted to evaluate the performance of the proposed method, and then the method is applied to predict ocean wave heights using wind conditions.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501098","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":"Global sensitivity and domain-selective testing for functional-valued responses: An application to climate economy models","authors":"Matteo Fontana,&nbsp;Massimo Tavoni,&nbsp;Simone Vantini","doi":"10.1002/env.2866","DOIUrl":"https://doi.org/10.1002/env.2866","url":null,"abstract":"<p>Understanding the dynamics and evolution of climate change and associated uncertainties is key for designing robust policy actions. Computer models are key tools in this scientific effort, which have now reached a high level of sophistication and complexity. Model auditing is needed in order to better understand their results, and to deal with the fact that such models are increasingly opaque with respect to their inner workings. Current techniques such as Global Sensitivity Analysis (GSA) are limited to dealing either with multivariate outputs, stochastic ones, or finite-change inputs. This limits their applicability to time-varying variables such as future pathways of greenhouse gases. To provide additional semantics in the analysis of a model ensemble, we provide an extension of GSA methodologies tackling the case of stochastic functional outputs with finite change inputs. To deal with finite change inputs and functional outputs, we propose an extension of currently available GSA methodologies while we deal with the stochastic part by introducing a novel, domain-selective inferential technique for sensitivity indices. Our method is explored via a simulation study that shows its robustness and efficacy in detecting sensitivity patterns. We apply it to real-world data, where its capabilities can provide to practitioners and policymakers additional information about the time dynamics of sensitivity patterns, as well as information about robustness.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174132","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":"Functional zoning of biodiversity profiles","authors":"Natalia Golini,&nbsp;Rosaria Ignaccolo,&nbsp;Luigi Ippoliti,&nbsp;Nicola Pronello","doi":"10.1002/env.2865","DOIUrl":"https://doi.org/10.1002/env.2865","url":null,"abstract":"<p>Spatial mapping of biodiversity is crucial to investigate spatial variations in natural communities. Several indices have been proposed in the literature to represent biodiversity as a single statistic. However, these indices only provide information on individual dimensions of biodiversity, thus failing to grasp its complexity comprehensively. Consequently, relying solely on these single indices can lead to misleading conclusions about the actual state of biodiversity. In this work, we focus on <i>biodiversity profiles</i>, which provide a more flexible framework to express biodiversity through nonnegative and convex curves, which can be analyzed by means of functional data analysis. By treating the whole curves as single entities, we propose to achieve a <i>functional zoning</i> of the region of interest by means of a penalized model-based clustering procedure. This provides a spatial clustering of the biodiversity profiles, which is useful for policy-makers both for conserving and managing natural resources and revealing patterns of interest. Our approach is evaluated using a simulation study and discussed through the analysis of the <i>Harvard Forest Data</i>, which provides information on the spatial distribution of woody stems within a plot of the Harvard Forest.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2865","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115318","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":"Catalysing virtual collaboration: The experience of the remote TIES working groups","authors":"M. Meis,&nbsp;M. Pirani,&nbsp;C. Euan,&nbsp;S. Castruccio,&nbsp;S. Simmons,&nbsp;J.R. Stroud,&nbsp;M. Blangiardo,&nbsp;C.K. Wikle,&nbsp;M. Wheeler,&nbsp;E. Naumova,&nbsp;L. Bravo,&nbsp;C. Miller,&nbsp;Y. Gel","doi":"10.1002/env.2855","DOIUrl":"10.1002/env.2855","url":null,"abstract":"<p>During the COVID-19 pandemic, the idea of collaboration and scientific exchange between members of the scientific community was enhanced by technology. Virtual meetings and work platforms have become common resources to continue generating research, partially replacing instances of joint in-person work before, during or after a conference. The idea of teleworking played a fundamental role in remote collaboration groups within The International Statistical Society (TIES), a community of interdisciplinary scientists such as statisticians, mathematicians, meteorologists, and biologists, among others working on quantitative methods to enhance solutions to environmental problems. In 2021 the Society launched three working groups with the aim of improving networking across the Society's members and develop creative collaboration, while advancing statistical and computational methods motivated by real-world driven applications in environmental research. Here, we provide insights from this virtual collaborative initiative.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 6","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141107983","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":"Bayesian benchmark dose risk assessment with mixed-factor quantal data","authors":"Mirjana Glisovic-Bensa,&nbsp;Walter W. Piegorsch,&nbsp;Edward J. Bedrick","doi":"10.1002/env.2854","DOIUrl":"10.1002/env.2854","url":null,"abstract":"<p>Benchmark analysis is a general risk estimation strategy for identifying the benchmark dose (BMD) past which the risk of exhibiting an adverse environmental response exceeds a fixed, target value of benchmark response. Estimation of BMD and of its lower confidence limit (BMDL) is well understood for the case of an adverse response to a single stimulus. In many environmental settings, however, one or more additional, secondary, qualitative factor(s) may collude to affect the adverse outcome, such that the risk changes with differential levels of the secondary factor. Bayesian methods for estimation of the BMD and BMDL have grown in popularity, and a large variety of candidate dose–response models is available for applying these methods. This article applies Bayesian strategies to a mixed-factor setting with a secondary qualitative factor possessing two levels to derive two-factor Bayesian BMDs and BMDLs. We present reparameterized dose–response models that allow for explicit use of prior information on the target parameter of interest, the BMD. We also enhance our Bayesian estimation technique for BMD analysis by applying Bayesian model averaging to produce the BMDs and BMDLs, overcoming associated questions of model adequacy when multimodel uncertainty is present. An example from environmental carcinogenicity testing illustrates the calculations.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113061","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":"High dimensional variable selection through group Lasso for multiple function-on-function linear regression: A case study in PM10 monitoring","authors":"Adelia Evangelista,&nbsp;Christian Acal,&nbsp;Ana M. Aguilera,&nbsp;Annalina Sarra,&nbsp;Tonio Di Battista,&nbsp;Sergio Palermi","doi":"10.1002/env.2852","DOIUrl":"10.1002/env.2852","url":null,"abstract":"<div>\u0000 \u0000 <p>Analyzing the effect of chemical and local meteorological variables over the behaviour in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>PM</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>10</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{PM}}_{10} $$</annotation>\u0000 </semantics></math> concentrations in the Abruzzo region (Italy), with the objective of forecasting and controlling air quality, motivates the current work. Given that the available data are curves that represent the day-to-day variations, a multiple function-on-function linear regression (MFFLR) model is considered. By assuming the Karhunen-Loève expansion, MFFLR model can be reduced to a classical linear regression model for each principal component of the functional response in terms of all principal components (PCs) of the functional predictors. In this sense, a regularization approach for functional principal component regression based on the merge of functional data analysis with group Lasso is proposed. This novel methodology allows to estimate the model and, simultaneously, select those relevant functional predictors with the functional response, where each functional independent variable is represented by a group of input variables derived by the PCs.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834432","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 zero-inflated Poisson spatial model with misreporting for wildfire occurrences in southern Italian municipalities","authors":"Serena Arima,&nbsp;Crescenza Calculli,&nbsp;Alessio Pollice","doi":"10.1002/env.2853","DOIUrl":"10.1002/env.2853","url":null,"abstract":"<p>We propose a Poisson model for zero-inflated spatial counts contaminated by measurement error: we accommodate the excess of zeroes in the counts, consider the possible under/over reporting of the response and account for the neighboring structure of spatial areal units. Bayesian inferences are provided by MCMC implementation through the R package NIMBLE. To evaluate the model performance, a simulation study is carried out under configurations that allow for structured and unstructured spatial random effects. The proposed model is applied to investigate the distribution of the counts of wildfire occurrences in the municipal areas of two neighboring Italian regions for the summer season 2021. Fire counts are obtained by processing MODIS satellite data, while several socio-economic and environmental-driven potential risk factors are also considered in the model formulation. Data from multiple sources with different spatial support are processed in order to comply with the municipal units. Results suggest the appropriateness of the approach and provide some insights on the features of wildfire occurrences.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"36 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834422","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":"Pointwise data depth for univariate and multivariate functional outlier detection","authors":"Cristian F. Jiménez-Varón,&nbsp;Fouzi Harrou,&nbsp;Ying Sun","doi":"10.1002/env.2851","DOIUrl":"10.1002/env.2851","url":null,"abstract":"<p>Data depth is an efficient tool for robustly summarizing the distribution of functional data and detecting potential magnitude and shape outliers. Commonly used functional data depth notions, such as the modified band depth and extremal depth, are estimated from pointwise depth for each observed functional observation. However, these techniques require calculating one single depth value for each functional observation, which may not be sufficient to characterize the distribution of the functional data and detect potential outliers. This article presents an innovative approach to make the best use of pointwise depth. We propose using the pointwise depth distribution for magnitude outlier visualization and the correlation between pairwise depth for shape outlier detection. Furthermore, a bootstrap-based testing procedure has been introduced for the correlation to test whether there is any shape outlier. The proposed univariate methods are then extended to bivariate functional data. The performance of the proposed methods is examined and compared to conventional outlier detection techniques by intensive simulation studies. In addition, the developed methods are applied to simulated solar energy datasets from a photovoltaic system. Results revealed that the proposed method offers superior detection performance over conventional techniques. These findings will benefit engineers and practitioners in monitoring photovoltaic systems by detecting unnoticed anomalies and outliers.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626408","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":"Scanner\u0000 : Simultaneously temporal trend and spatial cluster detection for spatial-temporal data","authors":"Xin Wang,&nbsp;Xin Zhang","doi":"10.1002/env.2849","DOIUrl":"10.1002/env.2849","url":null,"abstract":"<p>Identifying the underlying trajectory pattern in the spatial-temporal data analysis is a fundamental but challenging task. In this paper, we study the problem of simultaneously identifying temporal trends and spatial clusters of spatial-temporal trajectories. To achieve this goal, we propose a novel method named spatial clustered and sparse nonparametric regression (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Scanner</mi>\u0000 </mrow>\u0000 <annotation>$$ mathsf{Scanner} $$</annotation>\u0000 </semantics></math>). Our method leverages the B-spline model to fit the temporal data and penalty terms on spline coefficients to reveal the underlying spatial-temporal patterns. In particular, our method estimates the model by solving a doubly-penalized least square problem, in which we use a group sparse penalty for trend detection and a spanning tree-based fusion penalty for spatial cluster recovery. We also develop an algorithm based on the alternating direction method of multipliers (ADMM) algorithm to efficiently minimize the penalized least square loss. The statistical consistency properties of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Scanner</mi>\u0000 </mrow>\u0000 <annotation>$$ mathsf{Scanner} $$</annotation>\u0000 </semantics></math> estimator are established in our work. In the end, we conduct thorough numerical experiments to verify our theoretical findings and validate that our method outperforms the existing competitive approaches.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140615343","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":"Contamination severity index: An analysis of Bangladesh groundwater arsenic","authors":"Yogendra P. Chaubey,&nbsp;Qi Zhang","doi":"10.1002/env.2850","DOIUrl":"10.1002/env.2850","url":null,"abstract":"<p>This article deals with the measurement of groundwater arsenic (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>A</mi>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 <annotation>$$ As $$</annotation>\u0000 </semantics></math>) contamination. The focus is on using a proper index for the severity of contamination, rather than just using the proportion of observations above a threshold level. We specifically focus on the contamination severity index (CSI) proposed by Sen (2016. <i>Sankhya B</i>, 78B(2), 341–361.). An alternative estimator in contrast to the one given by Sen (2016. <i>Sankhya B</i>, 78B(2), 341–361.) is used here which is useful for a small number of observations. The data used is that collected by the British Geological Society and the Bangladesh Department of Public Health Engineering during 1997–2001. Their analysis was based on the simple proportion of the observations above a threshold level, whereas the CSI measure adequately takes into account the severity of the observations. It is emphasized in this article that the comparison of areas with average arsenic (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>A</mi>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 <annotation>$$ As $$</annotation>\u0000 </semantics></math>) levels to determine arsenic severity is not appropriate in general due to a large variation in the sample values due to the depth of wells. However, an alternative to the CSI proposed in Sen (2016. <i>Sankhya B</i>, 78B(2), 341–361.) has been given in this article that takes into account the depth of wells corresponding to the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>A</mi>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 <annotation>$$ As $$</annotation>\u0000 </semantics></math> samples. This article also uses the bootstrap methodology in assessing the bias and standard errors of the estimators, and the corresponding <i>bias-corrected and accelerated</i> confidence intervals.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140615298","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}