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Pointwise data depth for univariate and multivariate functional outlier detection Environmetrics (IF 1.7) Pub Date : 2024-04-20 Cristian F. Jiménez‐Varón, Fouzi Harrou, Ying Sun
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
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Contamination severity index: An analysis of Bangladesh groundwater arsenic Environmetrics (IF 1.7) Pub Date : 2024-04-17 Yogendra P. Chaubey, Qi Zhang
This article deals with the measurement of groundwater arsenic () 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. Sankhya B, 78B(2), 341–361.). An alternative estimator in contrast to the one given
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Scanner: Simultaneously temporal trend and spatial cluster detection for spatial‐temporal data Environmetrics (IF 1.7) Pub Date : 2024-04-17 Xin Wang, Xin Zhang
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 (). Our method leverages the B‐spline
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Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects Environmetrics (IF 1.7) Pub Date : 2024-04-16 Jesper Muren, Vilhelm Niklasson, Dmitry Otryakhin, Maxim Romashin
This article is devoted to the problem of detection of forest and nonforest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families, another one—on nonparametric tests. The parametric approach is novel in the literature and relevant to a larger class of problems—detection of natural objects, as
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Estimation and selection for spatial zero‐inflated count models Environmetrics (IF 1.7) Pub Date : 2024-04-05 Chung‐Wei Shen, Chun‐Shu Chen
The count data arise in many scientific areas. Our concerns here focus on spatial count responses with an excessive number of zeros and a set of available covariates. Estimating model parameters and selecting important covariates for spatial zero‐inflated count models are both essential. Importantly, to alleviate deviations from model assumptions, we propose a spatial zero‐inflated Poisson‐like methodology
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Testing for galactic cosmic ray warming hypothesis using the notion of block‐exogeneity Environmetrics (IF 1.7) Pub Date : 2024-03-31 Umberto Triacca
In this article, we consider the notion of block‐exogeneity and establish a characterization of it. We use this characterization to propose a procedure to test for block‐exogeneity in a trivariate system. The proposed procedure has been applied to test the so‐called galactic cosmic ray warming hypothesis. The galactic cosmic ray warming hypothesis suggests the existence of an indirect solar influence
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Fast parameter estimation of generalized extreme value distribution using neural networks Environmetrics (IF 1.7) Pub Date : 2024-03-12 Sweta Rai, Alexis Hoffman, Soumendra Lahiri, Douglas W. Nychka, Stephan R. Sain, Soutir Bandyopadhyay
The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires and so forth. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate-sized datasets. To overcome this limitation, we propose a computationally
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Recursive nearest neighbor co-kriging models for big multi-fidelity spatial data sets Environmetrics (IF 1.7) Pub Date : 2024-02-25 Si Cheng, Bledar A. Konomi, Georgios Karagiannis, Emily L. Kang
Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co-kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associated Bayesian inference for these models is usually facilitated via Markov chain Monte Carlo (MCMC) methods which present (sometimes prohibitively) slow mixing
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Sampling design methods for making improved lake management decisions Environmetrics (IF 1.7) Pub Date : 2024-02-08 Vilja Koski, Jo Eidsvik
The ecological status of lakes is important for understanding an ecosystem's biodiversity as well as for service water quality and policies related to land use and agricultural run-off. If the status is weak, then decisions about management alternatives need to be made. We assess the value of information of lake monitoring in Finland, where lakes are abundant. With reasonable ecological values and
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Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability Environmetrics (IF 1.7) Pub Date : 2024-02-01 Danielle Demateis, Kayleigh P. Keller, David Rojas-Rueda, Marianthi-Anna Kioumourtzoglou, Ander Wilson
Maternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollution and low birth weight. A popular method to estimate this association while identifying windows of susceptibility is a distributed lag model (DLM), which regresses an outcome onto exposure history observed at multiple time
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Structural equation models for simultaneous modeling of air pollutants Environmetrics (IF 1.7) Pub Date : 2024-01-14 Mariaelena Bottazzi Schenone, Elena Grimaccia, Maurizio Vichi
This paper provides a new modeling for air pollution, simultaneously taking into account the six main pollutants (PM10 and PM2.5, Sulphate Dioxide, Nitrogen Dioxide, Carbon Monoxide, ground level Ozone concentrations) and their key determinants, employing Structural Equation Models (SEMs). The model is able to estimate the complex links among air pollutants, often neglected in literature, and identifies
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Quantifying and correcting geolocation error in spaceborne LiDAR forest canopy observations using high spatial accuracy data: A Bayesian model approach Environmetrics (IF 1.7) Pub Date : 2024-01-08 Elliot S. Shannon, Andrew O. Finley, Daniel J. Hayes, Sylvia N. Noralez, Aaron R. Weiskittel, Bruce D. Cook, Chad Babcock
Geolocation error in spaceborne sampling light detection and ranging (LiDAR) measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced field measurements or other remotely sensed data. Data integration is especially problematic when geolocation error is not well quantified. We propose a general model that uses airborne laser scanning data
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Multivariate nearest-neighbors Gaussian processes with random covariance matrices Environmetrics (IF 1.7) Pub Date : 2024-01-02 Isabelle Grenier, Bruno Sansó, Jessica L. Matthews
We propose a non-stationary spatial model based on a normal-inverse-Wishart framework, conditioning on a set of nearest-neighbors. The model, called nearest-neighbor Gaussian process with random covariance matrices is developed for both univariate and multivariate spatial settings and allows for fully flexible covariance structures that impose no stationarity or isotropic restrictions. In addition
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Statistical evaluation of a long-memory process using the generalized entropic value-at-risk Environmetrics (IF 1.7) Pub Date : 2023-12-25 Hidekazu Yoshioka, Yumi Yoshioka
The modeling and identification of time series data with a long memory are important in various fields. The streamflow discharge is one such example that can be reasonably described as an aggregated stochastic process of randomized affine processes where the probability measure, we call it reversion measure, for the randomization is not directly observable. Accurate identification of the reversion
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New generalized extreme value distribution with applications to extreme temperature data Environmetrics (IF 1.7) Pub Date : 2023-12-14 Wilson Gyasi, Kahadawala Cooray
A new generalization of the extreme value distribution is presented with its density function, having a wide variety of density and tail shapes for modeling extreme value data. This generalized extreme value distribution will be referred to as the odd generalized extreme value distribution. It is derived by considering the distributions of the odds of the generalized extreme value distribution. Consequently
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Total least squares bias in climate fingerprinting regressions with heterogeneous noise variances and correlated explanatory variables Environmetrics (IF 1.7) Pub Date : 2023-12-12 Ross McKitrick
Regression-based “fingerprinting” methods in climate science employ total least squares (TLS) or orthogonal regression to remedy attenuation bias arising from measurement error due to reliance on climate model-generated explanatory variables. Proving the consistency of multivariate TLS requires assuming noise variances are equal across all variables in the model. This assumption has been challenged
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Temporal evolution of the extreme excursions of multivariate kth order Markov processes with application to oceanographic data Environmetrics (IF 1.7) Pub Date : 2023-12-03 Stan Tendijck, Philip Jonathan, David Randell, Jonathan Tawn
We develop two models for the temporal evolution of extreme events of multivariate k$$ k $$th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan and Tawn (Journal of the Royal Statistical Society: Series B (Methodology), 2014, 66, 497–546), and it naturally extends the work of Winter and Tawn (Journal of the Royal Statistical Society: Series
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Calibrated forecasts of quasi-periodic climate processes with deep echo state networks and penalized quantile regression Environmetrics (IF 1.7) Pub Date : 2023-11-20 Matthew Bonas, Christopher K. Wikle, Stefano Castruccio
Among the most relevant processes in the Earth system for human habitability are quasi-periodic, ocean-driven multi-year events whose dynamics are currently incompletely characterized by physical models, and hence poorly predictable. This work aims at showing how (1) data-driven, stochastic machine learning approaches provide an affordable yet flexible means to forecast these processes; (2) the associated
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Locally correlated Poisson sampling Environmetrics (IF 1.7) Pub Date : 2023-11-11 Wilmer Prentius
Designs that produces spatially balanced, or well-spread, samples are desirable as they increase the probability of obtaining a sample highly representative of the population. Spatially correlated Poisson sampling (SCPS) is a method for selecting well-spread samples. In the SCPS method, the sampling outcomes (inclusion or exclusion of units) are decided sequentially. After each decision, the inclusion
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Spatial regression modeling via the R2D2 framework Environmetrics (IF 1.7) Pub Date : 2023-10-27 Eric Yanchenko, Howard D. Bondell, Brian J. Reich
Spatially dependent data arises in many applications, and Gaussian processes are a popular modeling choice for these scenarios. While Bayesian analyses of these problems have proven to be successful, selecting prior distributions for these complex models remains a difficult task. In this work, we propose a principled approach for setting prior distributions on model variance components by placing a
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On the identifiability of the trinomial model for mark-recapture-recovery studies Environmetrics (IF 1.7) Pub Date : 2023-10-26 Simon J. Bonner, Wei Zhang, Jiaqi Mu
Continuous predictors of survival present a challenge in the analysis of data from studies of marked individuals if they vary over time and can only be observed when individuals are captured. Existing methods to study the effects of such variables have followed one of two approaches. The first is to model the joint distribution of the predictor and the observed capture histories, and the second is
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An extended PDE-based statistical spatio-temporal model that suppresses the Gibbs phenomenon Environmetrics (IF 1.7) Pub Date : 2023-10-26 Guanzhou Wei, Xiao Liu, Russell Barton
Partial differential equation (PDE)-based spatio-temporal models are available in the literature for modeling spatio-temporal processes governed by advection-diffusion equations. The main idea is to approximate the process by a truncated Fourier series and model the temporal evolution of the spectral coefficients by a stochastic process whose parametric structure is determined by the governing PDE
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Elastic functional changepoint detection of climate impacts from localized sources Environmetrics (IF 1.7) Pub Date : 2023-10-24 J. Derek Tucker, Drew Yarger
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 ( y $$ y $$ -axis) and phase ( x $$ x $$ -axis) variability. If not accounted for properly, true changepoints may be undetected, and the estimated underlying mean change functions
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Joint species distribution modeling with competition for space Environmetrics (IF 1.7) Pub Date : 2023-10-18 Juho Kettunen, Lauri Mehtätalo, Eeva-Stiina Tuittila, Aino Korrensalo, Jarno Vanhatalo
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
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A spatially-weighted AMH copula-based dissimilarity measure for clustering variables: An application to urban thermal efficiency Environmetrics (IF 1.7) Pub Date : 2023-10-17 F. Marta L. Di Lascio, Andrea Menapace, Roberta Pappadà
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
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Estimation of change with partially overlapping and spatially balanced samples Environmetrics (IF 1.7) Pub Date : 2023-09-12 Xin Zhao, Anton Grafström
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
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Bayesian spatio-temporal survival analysis for all types of censoring with application to a wildlife disease study Environmetrics (IF 1.7) Pub Date : 2023-08-01 Kehui Yao, Jun Zhu, Daniel J. O'Brien, Daniel Walsh
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
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A Bayesian spatio-temporal model for short-term forecasting of precipitation fields Environmetrics (IF 1.7) Pub Date : 2023-08-01 S. R. Johnson, S. E. Heaps, K. J. Wilson, D. J. Wilkinson
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
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Novel application of a process convolution approach for calibrating output from numerical models Environmetrics (IF 1.7) Pub Date : 2023-07-30 Maike Holthuijzen, Dave Higdon, Brian Beckage, Patrick J. Clemins
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
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Modeling temporally misaligned data across space: The case of total pollen concentration in Toronto Environmetrics (IF 1.7) Pub Date : 2023-07-23 Sara Zapata-Marin, Alexandra M. Schmidt, Scott Weichenthal, Eric Lavigne
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
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Bayesian functional emulation of CO2 emissions on future climate change scenarios Environmetrics (IF 1.7) Pub Date : 2023-07-20 Luca Aiello, Matteo Fontana, Alessandra Guglielmi
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
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Air pollution estimation under air stagnation—A case study of Beijing Environmetrics (IF 1.7) Pub Date : 2023-07-10 Ying Zhang, Song Xi Chen, Le Bao
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
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Estimating atmospheric motion winds from satellite image data using space-time drift models Environmetrics (IF 1.7) Pub Date : 2023-07-06 Indranil Sahoo, Joseph Guinness, Brian J. Reich
Geostationary weather satellites collect high-resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half-integers
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Long memory conditional random fields on regular lattices Environmetrics (IF 1.7) Pub Date : 2023-06-28 Angela Ferretti, L. Ippoliti, P. Valentini, R. J. Bhansali
This paper draws its motivation from applications in geophysics, agricultural, and environmental sciences where empirical evidence of slow decay of correlations have been found for data observed on a regular lattice. Spatial ARFIMA models represent a widely used class of spatial models for analyzing such data. Here, we consider their generalization to conditional autoregressive fractional integrated
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Spatio-temporal downscaling emulator for regional climate models Environmetrics (IF 1.7) Pub Date : 2023-06-12 Luis A. Barboza, Shu Wei Chou Chen, Marcela Alfaro Córdoba, Eric J. Alfaro, Hugo G. Hidalgo
Regional climate models (RCM) describe the mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from general circulation models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of compute
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Generalized gamma ARMA process for synthetic aperture radar amplitude and intensity data Environmetrics (IF 1.7) Pub Date : 2023-06-10 Willams B. F. da Silva, Pedro M. Almeida-Junior, Abraão D. C. Nascimento
We propose a new autoregressive moving average (ARMA) process with generalized gamma (G Γ $$ \Gamma $$ ) marginal law, called G Γ $$ \Gamma $$ -ARMA. We derive some of its mathematical properties: moment-based closed-form expressions, score function, and Fisher information matrix. We provide a procedure for obtaining maximum likelihood estimates for the G Γ $$ \Gamma $$ -ARMA parameters. Its performance
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Bayesian geostatistical modeling for discrete-valued processes Environmetrics (IF 1.7) Pub Date : 2023-06-02 Xiaotian Zheng, Athanasios Kottas, Bruno Sansó
We introduce a flexible and scalable class of Bayesian geostatistical models for discrete data, based on nearest-neighbor mixture processes (NNMP), referred to as discrete NNMP. To define the joint probability mass function (pmf) over a set of spatial locations, we build from local mixtures of conditional pmfs using a directed graphical model, with a directed acyclic graph that summarizes the nearest
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Assessing the ability of adaptive designs to capture trends in hard coral cover Environmetrics (IF 1.7) Pub Date : 2023-05-08 AWLP Thilan, P Menéndez, JM McGree
Coral reefs have become one of the most vulnerable ecosystems worldwide due to rising environmental and anthropogenic pressures. Methods from experimental design can be used to furnish our ability to monitor such ecosystems efficiently. Recently, adaptive design approaches have been proposed for monitoring coral reefs; however, questions have surfaced around the ability of such approaches to capture
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A hierarchical Bayesian non-asymptotic extreme value model for spatial data Environmetrics (IF 1.7) Pub Date : 2023-05-04 Federica Stolf, Antonio Canale
Spatial maps of extreme precipitation are crucial in flood prevention. With the aim of producing maps of precipitation return levels, we propose a novel approach to model a collection of spatially distributed time series where the asymptotic assumption, typical of the traditional extreme value theory, is relaxed. We introduce a Bayesian hierarchical model that accounts for the possible underlying variability
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CO2 has significant implications for hourly ambient temperature: Evidence from Hawaii Environmetrics (IF 1.7) Pub Date : 2023-05-03 Kevin F. Forbes
A small group of climate scientists and influencers have vigorously disputed the scientific consensus on climate change. They have contributed to a belief system that has impeded policy actions to reduce emissions. They accept that more CO2 in the atmosphere has consequences for the climate but strongly deny that the magnitude of the effect is significant. Using hourly CO2 data from the Mauna Loa Observatory
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Detection of anomalous radioxenon concentrations: A distribution-free approach Environmetrics (IF 1.7) Pub Date : 2023-04-24 Michele Scagliarini, Rosanna Gualdi, Giuseppe Ottaviano, Antonietta Rizzo
The detection of anomalous atmospheric radioxenon concentrations plays a key role in detecting both underground nuclear explosions and radioactive emissions from nuclear power plants and medical isotope production facilities. For this purpose, the CTBTO's International Data Centre uses a procedure based on descriptive thresholds. In order to supplement this procedure with a statistical inference-based
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Mitigating spatial confounding by explicitly correlating Gaussian random fields Environmetrics (IF 1.7) Pub Date : 2023-03-27 Isa Marques, Thomas Kneib, Nadja Klein
In the fourth column under the row “MGRF” in Table 1 of Marques et al. (2022) the mean value was incorrect in the original published article. The mean value should read “−0.143” and not “0.143.” The correct table appears below: TABLE 1. Mean and equal-tailed 95% credible interval (CI) for the posterior of β 1 obs and β 2 obs in the five models considered. Model β 1 obs (elevation) β 2 obs (temperature)
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Subordinated Gaussian processes for solar irradiance Environmetrics (IF 1.7) Pub Date : 2023-03-23 Caitlin M. Berry, William Kleiber, Bri-Mathias Hodge
Traditionally the power grid has been a one-way street with power flowing from large transmission-connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers
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Multistage hierarchical capture–recapture models Environmetrics (IF 1.7) Pub Date : 2023-03-20 Mevin B. Hooten, Michael R. Schwob, Devin S. Johnson, Jacob S. Ivan
Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual-level data. A variety of implementations exist for such models, including integrated likelihood, parameter-expanded data augmentation, and combinations of those. Capture–recapture models with latent random
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Approximation of Bayesian Hawkes process with inlabru Environmetrics (IF 1.7) Pub Date : 2023-03-14 Francesco Serafini, Finn Lindgren, Mark Naylor
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
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New estimation methods for extremal bivariate return curves Environmetrics (IF 1.7) Pub Date : 2023-02-17 C. J. R. Murphy-Barltrop, J. L. Wadsworth, E. F. Eastoe
In the multivariate setting, estimates of extremal risk measures are important in many contexts, such as environmental planning and structural engineering. In this paper, we propose new estimation methods for extremal bivariate return curves, a risk measure that is the natural bivariate extension to a return level. Unlike several existing techniques, our estimates are based on bivariate extreme value
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Environmental data science: Part 2 Environmetrics (IF 1.7) Pub Date : 2023-02-16 Wesley S. Burr, Nathaniel K. Newlands, Andrew Zammit-Mangion
Environmental data science is a multi-disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two-part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses
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Comparing emulation methods for a high-resolution storm surge model Environmetrics (IF 1.7) Pub Date : 2023-02-14 Grant Hutchings, Bruno Sansó, James Gattiker, Devin Francom, Donatella Pasqualini
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
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CO2 emissions and growth: A bivariate bidimensional mean-variance random effects model Environmetrics (IF 1.7) Pub Date : 2023-02-11 Antonello Maruotti, Pierfrancesco Alaimo Di Loro
We introduce a bivariate bidimensional mixed-effects regression model, motivated by the analysis of CO 2 $$ {\mathrm{CO}}_2 $$ 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
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A Bayesian change point modeling approach to identify local temperature changes related to urbanization Environmetrics (IF 1.7) Pub Date : 2023-02-11 C. Berrett, B. Gurney, D. Arthur, T. Moon, G. P. Williams
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
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Smooth copula-based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada Environmetrics (IF 1.7) Pub Date : 2023-02-11 Fatima Palacios-Rodriguez, Elena Di Bernardino, Melina Mailhot
This paper proposes a smooth copula-based Generalized Extreme Value (GEV) model to map and predict extreme rainfall in Central Eastern Canada. The considered data contains a large portion of missing values, and one observes several nonconcomitant record periods at different stations. The proposed two-step approach combines GEV parameters' smooth functions in space through the use of spatial covariates
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Nonlinear prediction of functional time series Environmetrics (IF 1.7) Pub Date : 2023-02-05 Haixu Wang, Jiguo Cao
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
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Front Cover Image, Volume 34, Number 1, February 2023 Environmetrics (IF 1.7) Pub Date : 2023-01-29 Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun
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Environmental data science: Part 1 Environmetrics (IF 1.7) Pub Date : 2023-01-29 Andrew Zammit-Mangion, Nathaniel K. Newlands, Wesley S. Burr
Environmental data science is a multi-disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two-part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses
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The role of data science in environmental digital twins: In praise of the arrows Environmetrics (IF 1.7) Pub Date : 2023-01-26 Gordon S. Blair, Peter A. Henrys
Digital twins are increasingly important in many domains, including for understanding and managing the natural environment. Digital twins of the natural environment are fueled by the unprecedented amounts of environmental data now available from a variety of sources from remote sensing to potentially dense deployment of earth-based sensors. Because of this, data science techniques inevitably have a
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Families of complex-valued covariance models through integration Environmetrics (IF 1.7) Pub Date : 2023-01-13 Sandra De Iaco
In geostatistics, the theory of complex-valued random fields is often used to provide an appropriate characterization of vector data with two components. In this context, constructing new classes of complex covariance models to be used in structural analysis and, then for stochastic interpolation or simulation, represents a focus of particular interest in the scientific community and in many areas
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A Bayesian time series model for reconstructing hydroclimate from multiple proxies Environmetrics (IF 1.7) Pub Date : 2023-01-06 Niamh Cahill, Jacky Croke, Micheline Campbell, Kate Hughes, John Vitkovsky, Jack Eaton Kilgallen, Andrew Parnell
We propose a Bayesian model which produces probabilistic reconstructions of hydroclimatic variability in Queensland Australia. The model provides a standardized approach to hydroclimate reconstruction using multiple palaeoclimate proxy records derived from natural archives such as speleothems, ice cores and tree rings. The method combines time-series modeling with inverse prediction to quantify the
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Multivariate receptor modeling with widely dispersed Lichens as bioindicators of air quality Environmetrics (IF 1.7) Pub Date : 2022-12-25 Matthew Heiner, Taylor Grimm, Hayden Smith, Steven D. Leavitt, William F. Christensen, Gregory T. Carling, Larry L. St. Clair
Biomonitoring studies evaluating air quality via airborne element accumulation patterns in lichens typically control variability by focusing on narrow geographic regions and short time windows. Using samples of the widespread “rock-posy” lichen sampled across the Intermountain Region of the United States, we investigate whether accumulation patterns of generic pollution sources are detectable on broad