Environmetrics最新文献_第7页

Multivariate nearest-neighbors Gaussian processes with random covariance matrices 具有随机协方差矩阵的多变量近邻高斯过程

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2024-01-02 DOI: 10.1002/env.2839

Isabelle Grenier, Bruno Sansó, Jessica L. Matthews

引用次数: 0

Statistical evaluation of a long-memory process using the generalized entropic value-at-risk 利用广义熵风险值对长记忆过程进行统计评估

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-12-25 DOI: 10.1002/env.2838

Hidekazu Yoshioka, Yumi Yoshioka

{"title":"Statistical evaluation of a long-memory process using the generalized entropic value-at-risk","authors":"Hidekazu Yoshioka, Yumi Yoshioka","doi":"10.1002/env.2838","DOIUrl":"10.1002/env.2838","url":null,"abstract":"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 measure is critical because of its omnipresence in the aggregated stochastic process. However, the modeling accuracy is commonly limited by the available real-world data. We resolve this issue by proposing the novel upper and lower bounds of a statistic of interest subject to ambiguity of the reversion measure. Here, we use the Tsallis value-at-risk (TsVaR) as a convex risk functional to generalize the widely used entropic value-at-risk (EVaR) as a sharp statistical indicator. We demonstrate that the EVaR cannot be used for evaluating key statistics, such as mean and variance, of the streamflow discharge due to the blowup of some exponential integrand. We theoretically show that the TsVaR can avoid this issue because it requires only the existence of some polynomial moment, not exponential moment. As a demonstration, we apply the semi-implicit gradient descent method to calculate the TsVaR and corresponding Radon–Nikodym derivative for time series data of actual streamflow discharges in mountainous river environments.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New generalized extreme value distribution with applications to extreme temperature data 应用于极端温度数据的新广义极值分布

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-12-14 DOI: 10.1002/env.2836

Wilson Gyasi, Kahadawala Cooray

{"title":"New generalized extreme value distribution with applications to extreme temperature data","authors":"Wilson Gyasi, Kahadawala Cooray","doi":"10.1002/env.2836","DOIUrl":"10.1002/env.2836","url":null,"abstract":"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, the new distribution is enlightened by not only having all six families of extreme value distributions; Gumbel, Fréchet, Weibull, reverse-Gumbel, reverse-Fréchet, and reverse-Weibull as submodels but also convenient for modeling bimodal extreme value data that are frequently found in environmental sciences. Basic properties of the distribution, including tail behavior and tail heaviness, are studied. Also, quantile-based aliases of the new distribution are illustrated using Galton's skewness and Moor's kurtosis plane. The adequacy of the new distribution is illustrated using well-known goodness-of-fit measures. A simulation is performed to validate the estimated risk measures due to repeated data points frequently found in temperature data. The Grand Rapids and well-known Wooster temperature data sets are analyzed and compared to nine different extreme value distributions to illustrate the new distribution's bimodality, flexibility, and overall fitness.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138628052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Total least squares bias in climate fingerprinting regressions with heterogeneous noise variances and correlated explanatory variables 具有异质噪声方差和相关解释变量的气候指纹回归中的总最小二乘法偏差

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-12-12 DOI: 10.1002/env.2835

Ross McKitrick

{"title":"Total least squares bias in climate fingerprinting regressions with heterogeneous noise variances and correlated explanatory variables","authors":"Ross McKitrick","doi":"10.1002/env.2835","DOIUrl":"10.1002/env.2835","url":null,"abstract":"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 empirically in the climate context but little is known about TLS biases when the assumption is violated. Monte Carlo analysis is used herein to examine coefficient biases when the noise variances are not equal. The analysis allows the explanatory variables to be negatively correlated which is typical in climate applications. Ordinary least squares (OLS) exhibits the expected attenuation bias which vanishes as the noise variances on the explanatory variables disappear. In some cases, TLS corrects attenuation bias but more typically imparts large and generally positive biases. OLS performs well when the true value of <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 <mo>=</mo>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 <annotation>$$ beta =0 $$</annotation>\u0000 </semantics></math> whereas TLS performs quite poorly. This implies that TLS is not well suited for tests of the null. When <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 <mo>=</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation>$$ beta =1 $$</annotation>\u0000 </semantics></math> TLS tends to exhibit opposite biases to OLS. Diagnostic information specific to each data sample should be consulted before using TLS to avoid spurious inferences and replacing OLS attenuation bias with other, potentially larger biases.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138631025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal evolution of the extreme excursions of multivariate k $$ k $$ th order Markov processes with application to oceanographic data 多元k $$ k $$阶马尔可夫过程极值漂移的时间演化及其在海洋资料中的应用

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-12-03 DOI: 10.1002/env.2834

Stan Tendijck, Philip Jonathan, David Randell, Jonathan Tawn

{"title":"Temporal evolution of the extreme excursions of multivariate \u0000 \u0000 \u0000 k\u0000 \u0000 $$ k $$\u0000 th order Markov processes with application to oceanographic data","authors":"Stan Tendijck, Philip Jonathan, David Randell, Jonathan Tawn","doi":"10.1002/env.2834","DOIUrl":"10.1002/env.2834","url":null,"abstract":"We develop two models for the temporal evolution of extreme events of multivariate <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ k $$</annotation>\u0000 </semantics></math>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 C (Applied Statistics), 2016, 65, 345–365; Extremes, 2017, 20, 393–415) and Tendijck et al. (Environmetrics 2019, 30, e2541) to include multivariate random variables. We use cross-validation-type techniques to develop a model order selection procedure, and we test our models on two-dimensional meteorological-oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly-developed models perform better than the widely used historical matching methodology for these data.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Calibrated forecasts of quasi-periodic climate processes with deep echo state networks and penalized quantile regression 基于深度回波状态网络和惩罚分位数回归的准周期气候过程校准预报

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-11-20 DOI: 10.1002/env.2833

Matthew Bonas, Christopher K. Wikle, Stefano Castruccio

引用次数: 0

Locally correlated Poisson sampling 局部相关泊松采样

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-11-11 DOI: 10.1002/env.2832

Wilmer Prentius

引用次数: 0

Spatial regression modeling via the R2D2 framework 通过 R2D2 框架建立空间回归模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-10-27 DOI: 10.1002/env.2829

Eric Yanchenko, Howard D. Bondell, Brian J. Reich

{"title":"Spatial regression modeling via the R2D2 framework","authors":"Eric Yanchenko, Howard D. Bondell, Brian J. Reich","doi":"10.1002/env.2829","DOIUrl":"10.1002/env.2829","url":null,"abstract":"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 prior distribution on a measure of model fit. In particular, we derive the distribution of the prior coefficient of determination. Placing a beta prior distribution on this measure induces a generalized beta prime prior distribution on the global variance of the linear predictor in the model. This method can also be thought of as shrinking the fit towards the intercept-only (null) model. We derive an efficient Gibbs sampler for the majority of the parameters and use Metropolis–Hasting updates for the others. Finally, the method is applied to a marine protection area dataset. We estimate the effect of marine policies on biodiversity and conclude that no-take restrictions lead to a slight increase in biodiversity and that the majority of the variance in the linear predictor comes from the spatial effect.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2829","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An extended PDE-based statistical spatio-temporal model that suppresses the Gibbs phenomenon 抑制吉布斯现象的基于 PDE 的扩展统计时空模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-10-26 DOI: 10.1002/env.2831

Guanzhou Wei, Xiao Liu, Russell Barton

{"title":"An extended PDE-based statistical spatio-temporal model that suppresses the Gibbs phenomenon","authors":"Guanzhou Wei, Xiao Liu, Russell Barton","doi":"10.1002/env.2831","DOIUrl":"10.1002/env.2831","url":null,"abstract":"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. However, because many spatio-temporal processes are nonperiodic with boundary discontinuities, the truncation of Fourier series leads to the well-known Gibbs phenomenon (GP) in the output generated by the existing PDE-based approaches. This article shows that the existing PDE-based approach can be extended to suppress GP. The proposed approach starts with a data flipping procedure for the process respectively along the horizontal and vertical directions, as if we were unfolding a piece of paper folded twice along the two directions. For the flipped process, this article extends the existing PDE-based spatio-temporal model by obtaining the new temporal dynamics of the spectral coefficients. Because the flipped process is spatially periodic and has a complete waveform without boundary discontinuities, GP is removed even if the Fourier series is truncated. Numerical investigations show that the extended approach improves the modeling and prediction accuracy. Computer code is made available on GitHub.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135017579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the identifiability of the trinomial model for mark-recapture-recovery studies 论标记-再捕获-再恢复研究中三叉模型的可识别性

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-10-26 DOI: 10.1002/env.2827

Simon J. Bonner, Wei Zhang, Jiaqi Mu

{"title":"On the identifiability of the trinomial model for mark-recapture-recovery studies","authors":"Simon J. Bonner, Wei Zhang, Jiaqi Mu","doi":"10.1002/env.2827","DOIUrl":"10.1002/env.2827","url":null,"abstract":"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 to draw inference from the likelihood conditional on events that depend only on observed predictor values, called the trinomial model. Previous comparison of these approaches found that joint modelling provided more precise inference about the effect of the covariate while the trinomial model was less prone to issues of model mis-specification. However, we believe that an important issue was missed. We show through mathematical analysis and numerical simulation that the trinomial model is not identifiable when the predictor has no effect on the survival probability. This also causes inferences from the trinomial model to be imprecise when the effect of the covariate on the survival probability is small. We also analyse data on the effect of body mass on the survival of meadow voles to demonstrate the importance of this issue in real applications.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"35 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135017834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0