Spatial Statistics最新文献_第10页

Softening the criteria for determining inner and outer predicted exceedance sets 软化确定内部和外部预测超出集的标准

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-03-01 Epub Date: 2024-12-17 DOI: 10.1016/j.spasta.2024.100876

Thomas Suesse , Alexander Brenning

{"title":"Softening the criteria for determining inner and outer predicted exceedance sets","authors":"Thomas Suesse , Alexander Brenning","doi":"10.1016/j.spasta.2024.100876","DOIUrl":"10.1016/j.spasta.2024.100876","url":null,"abstract":"<div><div>Determining exceedance regions, such as regions where a specified threshold of a pollutant in the environment is exceeded, is of critical importance for decision-making in environmental management and public health. Inner and outer predicted exceedance sets express the uncertainties in predicted exceedance regions as they sandwich the unknown true exceedance region with high confidence, analogous to confidence regions for point estimates. It is therefore desirable to reduce the uncertainty about the locations of the true exceedance region, resulting in a narrow band between the inner and outer sets. However, in practice this is not often the case mainly due to the strict statistical subset criteria being set, which are equivalent to a multiple testing problem controlling the familywise error rate (FWER). It is well known that the FWER leads to fewer rejections compared to other criteria; in the context of exceedance regions, this would correspond to an extremely small, conservative inner predicted exceedance region. In this paper, we loosen the criteria slightly to obtain a narrower band between inner and outer sets, allowing for more nuanced uncertainty assessments. A new algorithm is proposed to construct these exceedance sets, and the methods are compared in a simulation study to assess whether they indeed control the new criteria. The methods are illustrated on two data sets: average rainfall in the state of Paraná, Brazil, and nitrogen dioxide air pollution in Germany in the year 2018.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100876"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Epidemiological insights and geographic clusters for COVID-19 in Taiwan using a mixture scan statistic 利用混合扫描统计法洞察台湾 COVID-19 的流行病学和地理集群

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-13 DOI: 10.1016/j.spasta.2024.100871

Yi-Hung Kung

{"title":"Epidemiological insights and geographic clusters for COVID-19 in Taiwan using a mixture scan statistic","authors":"Yi-Hung Kung","doi":"10.1016/j.spasta.2024.100871","DOIUrl":"10.1016/j.spasta.2024.100871","url":null,"abstract":"<div><div>The COVID-19 pandemic has posed unprecedented public health challenges worldwide, necessitating a comprehensive understanding of its transmission dynamics. This study examines the correlation between COVID-19 transmission and various risk factors, focusing on the impact of population structure and socio-economic conditions in Taiwan. By analyzing official government databases, we explore how factors such as population density, dependency ratios, and socio-economic environment influence the spread of COVID-19. Our findings highlight that densely populated areas, along with regions characterized by higher child dependency ratios and a significant number of low- and middle-income households, exhibit higher transmission rates. This research underscores the importance of considering socio-economic disparities and healthcare access in developing effective public health strategies. Furthermore, we utilize a mixture scan statistic to identify disease hotspots, taking into account spatial correlation and covariate effects. This approach can detect clusters based on known risk factors and help to assess possible unknown geographic risks, facilitating targeted interventions and resource allocation. Our study contributes to the broader understanding of COVID-19 transmission dynamics, offering insights into the importance of integrating socio-economic factors and spatial analysis in pandemic response efforts.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100871"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach 利用 INLA-SPDE 方法进行时空数据融合，以分析原地数据和遥感数据

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-30 DOI: 10.1016/j.spasta.2024.100863

Shiyu He, Samuel W.K. Wong

{"title":"Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach","authors":"Shiyu He, Samuel W.K. Wong","doi":"10.1016/j.spasta.2024.100863","DOIUrl":"10.1016/j.spasta.2024.100863","url":null,"abstract":"<div><div>We propose a Bayesian hierarchical model to address the challenge of spatial misalignment in spatio-temporal data obtained from in situ and satellite sources. The model is fit using the INLA-SPDE approach, which provides efficient computation. Our methodology combines the different data sources in a “fusion” model via the construction of projection matrices in both spatial and temporal domains. Through simulation studies, we demonstrate that the fusion model has superior performance in prediction accuracy across space and time compared to standalone “in situ” and “satellite” models based on only in situ or satellite data, respectively. The fusion model also generally outperforms the standalone models in terms of parameter inference. Such a modeling approach is motivated by environmental problems, and our specific focus is on the analysis and prediction of harmful algae bloom (HAB) events, where the convention is to conduct separate analyses based on either in situ samples or satellite images. A real data analysis shows that the proposed model is a necessary step towards a unified characterization of bloom dynamics and identifying the key drivers of HAB events.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"64 ","pages":"Article 100863"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model 利用非参数、非稳态空间模型对集合卡尔曼滤波器进行规范化处理

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-11-09 DOI: 10.1016/j.spasta.2024.100870

Michael Tsyrulnikov , Arseniy Sotskiy

{"title":"Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model","authors":"Michael Tsyrulnikov , Arseniy Sotskiy","doi":"10.1016/j.spasta.2024.100870","DOIUrl":"10.1016/j.spasta.2024.100870","url":null,"abstract":"<div><div>The sample covariance matrix of a random vector is a good estimate of the true covariance matrix if the sample size is much larger than the length of the vector. In high-dimensional problems, this condition is never met. As a result, in high dimensions the Ensemble Kalman Filter’s (EnKF) ensemble does not contain enough information to specify the prior covariance matrix accurately. This necessitates the need for regularization of the analysis (observation update) problem. We propose a regularization technique based on a new spatial model. The model is a constrained version of the general Gaussian process convolution model. The constraints include local stationarity and smoothness of local spectra. We regularize EnKF by postulating that its prior covariances obey the spatial model. Placing a hyperprior distribution on the model parameters and using the likelihood of the prior ensemble data allows for an optimized use of both the ensemble and the hyperprior. A linear version of the respective estimator is shown to be consistent. A more accurate nonlinear neural-Bayes implementation of the estimator is developed. In simulation experiments, the new technique led to substantially better EnKF performance than several existing techniques.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"64 ","pages":"Article 100870"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-stationary spatio-temporal modeling using the stochastic advection–diffusion equation 利用随机平流扩散方程进行非稳态时空建模

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1016/j.spasta.2024.100867

Martin Outzen Berild, Geir-Arne Fuglstad

引用次数: 0

Nonparametric isotropy test for spatial point processes using random rotations 利用随机旋转对空间点过程进行非参数各向同性检验

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-09-12 DOI: 10.1016/j.spasta.2024.100858

Chiara Fend, Claudia Redenbach

引用次数: 0

Estimation and inference of multi-effect generalized geographically and temporally weighted regression models 多效应广义地理和时间加权回归模型的估计和推论

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-02 DOI: 10.1016/j.spasta.2024.100861

Zhi Zhang , Ruochen Mei , Changlin Mei

{"title":"Estimation and inference of multi-effect generalized geographically and temporally weighted regression models","authors":"Zhi Zhang , Ruochen Mei , Changlin Mei","doi":"10.1016/j.spasta.2024.100861","DOIUrl":"10.1016/j.spasta.2024.100861","url":null,"abstract":"<div><div>Geographically and temporally weighted regression (GTWR) models have been an effective tool for exploring spatiotemporal heterogeneity of regression relationships. However, they cannot effectively model such response variables that follows discrete distributions. In this study, we first extend the distributions of response variables to one-parameter exponential family of distributions and formulate generalized geographically and temporally weighted regression (GGTWR) models with their unilaterally temporally weighted maximum likelihood estimation method. Furthermore, we propose so-called multi-effect GGTWR (MEGGTWR) models in which spatiotemporally varying, constant, temporally varying, and spatially varying coefficients may simultaneously be included to reflect different effects of explanatory variables. A coefficient-average-based estimation method is suggested to calibrate MEGGTWR models and a generalized likelihood ratio statistic based test is formulated to identify the types of coefficients. Simulation studies are then conducted to assess the performance of the proposed estimation and inference methods with the impact of multicollinearity among explanatory variables also examined. The results show that the estimation method for MEGGTWR models can accurately estimate various types of coefficients and the test method is of valid type <span><math><mi>I</mi></math></span> error and satisfactory power. Finally, the relationship between childhood hand, foot, and mouth disease cases and climate factors is analyzed by the proposed models with their estimation and inference methods and some interesting spatiotemporal patterns are uncovered.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"64 ","pages":"Article 100861"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A flexible class of priors for orthonormal matrices with basis function-specific structure 一类灵活的正交矩阵先验，具有基函数特定结构

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-11-12 DOI: 10.1016/j.spasta.2024.100866

Joshua S. North , Mark D. Risser , F. Jay Breidt

{"title":"A flexible class of priors for orthonormal matrices with basis function-specific structure","authors":"Joshua S. North , Mark D. Risser , F. Jay Breidt","doi":"10.1016/j.spasta.2024.100866","DOIUrl":"10.1016/j.spasta.2024.100866","url":null,"abstract":"<div><div>Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure <em>a priori</em>. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"64 ","pages":"Article 100866"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncovering hidden alignments in two-dimensional point fields 揭示二维点域中的隐藏排列

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1016/j.spasta.2024.100868

Eulogio Pardo-Igúzquiza , Peter A. Dowd

引用次数: 0

Variable selection of nonparametric spatial autoregressive models via deep learning 通过深度学习选择非参数空间自回归模型的变量

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-16 DOI: 10.1016/j.spasta.2024.100862

Xiaodi Zhang, Yunquan Song

引用次数: 0