Spatial Statistics最新文献_第8页

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 : 2024-10-02 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":"2024-10-02","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 spatio-temporal model for temporal evolution of spatial extremal dependence 空间极值依赖性时间演化的时空模型

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-09-30 DOI: 10.1016/j.spasta.2024.100860

Véronique Maume-Deschamps , Pierre Ribereau , Manal Zeidan

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 : 2024-09-12 DOI: 10.1016/j.spasta.2024.100858

Chiara Fend, Claudia Redenbach

引用次数: 0

Spatio-temporal clustering using generalized lasso to identify the spread of Covid-19 in Indonesia according to provincial flight route-based connections 利用广义套索进行时空聚类，根据各省航班航线的连接情况确定 Covid-19 在印度尼西亚的传播情况

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-09-04 DOI: 10.1016/j.spasta.2024.100857

Septian Rahardiantoro, Sachnaz Desta Oktarina, Anang Kurnia, Nickyta Shavira Maharani, Alfidhia Rahman Nasa Juhanda

引用次数: 0

Spatial statistics: Climate and the environment 空间统计：气候与环境

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-17 DOI: 10.1016/j.spasta.2024.100856

Christopher K. Wikle , Mevin B. Hooten , William Kleiber , Douglas W. Nychka

引用次数: 0

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-14 DOI: 10.1016/j.spasta.2024.100855

Daniel A. Griffith

{"title":"Self-correlated spatial random variables: From an auto- to a sui- model respecification","authors":"Daniel A. Griffith","doi":"10.1016/j.spasta.2024.100855","DOIUrl":"10.1016/j.spasta.2024.100855","url":null,"abstract":"<div><p>This paper marks the 50-year publication anniversary of Besag's seminal spatial auto- models paper. His classic article synthesizes generic autoregressive specifications (i.e., a response variable appears on both sides of a regression equation and/or probability function equal sign) for the following six popular random variables: normal, logistic (i.e., Bernoulli), binomial, Poisson, exponential, and gamma. Besag dismisses these last two while recognizing failures of both as well as the more scientifically critical counts-oriented auto-Poisson. His initially unsuccessful subsequent work first attempted to repair them (e.g., pseudo-likelihood estimation), and then successfully revise them within the context of mixed models, formulating a spatially structured random effects term that effectively and efficiently absorbs and accounts for spatial autocorrelation in geospatial data. One remaining weakness of all but the auto-normal is a need to resort to Markov chain Monte Carlo (MCMC) techniques for legitimate estimation purposes. Recently, Griffith succeeded in devising an innovative uniform distribution genre—sui-uniform random variables—that accommodates spatial autocorrelation, too. Its most appealing feature is that, by applying two powerful mathematical statistical theorems (i.e., the probability integral transform, and the quantile function), it redeems Besag's auto- model failures. This paper details conversion of Besag's initial six modified variates, exemplifying them with both simulation experiments and publicly accessible real-world georeferenced data. The principal outcome is valuable spatial statistical advancements, with special reference to Moran eigenvector spatial filtering.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"63 ","pages":"Article 100855"},"PeriodicalIF":2.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083754","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

Covariate-dependent spatio-temporal covariance models 随变量变化的时空协方差模型

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-10 DOI: 10.1016/j.spasta.2024.100853

Yen-Shiu Chin , Nan-Jung Hsu , Hsin-Cheng Huang

{"title":"Covariate-dependent spatio-temporal covariance models","authors":"Yen-Shiu Chin , Nan-Jung Hsu , Hsin-Cheng Huang","doi":"10.1016/j.spasta.2024.100853","DOIUrl":"10.1016/j.spasta.2024.100853","url":null,"abstract":"<div><p>Geostatistical regression models are widely used in environmental and geophysical sciences to characterize the mean and dependence structures for spatio-temporal data. Traditionally, these models account for covariates solely in the mean structure, neglecting their potential impact on the spatio-temporal covariance structure. This paper addresses a significant gap in the literature by proposing a novel covariate-dependent covariance model within the spatio-temporal random-effects model framework. Our approach integrates covariates into the covariance function through a Cholesky-type decomposition, ensuring compliance with the positive-definite condition. We employ maximum likelihood for parameter estimation, complemented by an efficient expectation conditional maximization algorithm. Simulation studies demonstrate the superior performance of our method compared to conventional techniques that ignore covariates in spatial covariances. We further apply our model to a PM<sub>2.5</sub> dataset from Taiwan, highlighting wind speed’s pivotal role in influencing the spatio-temporal covariance structure. Additionally, we incorporate wind speed and sunshine duration into the covariance function for analyzing Taiwan ozone data, revealing a more intricate relationship between covariance and these meteorological variables.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"63 ","pages":"Article 100853"},"PeriodicalIF":2.1,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048764","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 ecological models via physics-informed neural networks for studying chronic wasting disease 通过物理信息神经网络建立时空生态模型，用于研究慢性消耗性疾病

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-01 DOI: 10.1016/j.spasta.2024.100850

Juan Francisco Mandujano Reyes , Ting Fung Ma , Ian P. McGahan , Daniel J. Storm , Daniel P. Walsh , Jun Zhu

{"title":"Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease","authors":"Juan Francisco Mandujano Reyes , Ting Fung Ma , Ian P. McGahan , Daniel J. Storm , Daniel P. Walsh , Jun Zhu","doi":"10.1016/j.spasta.2024.100850","DOIUrl":"10.1016/j.spasta.2024.100850","url":null,"abstract":"<div><p>To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINNs) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatio-temporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"62 ","pages":"Article 100850"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846617","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

Flexible basis representations for modeling large non-Gaussian spatial data 为大型非高斯空间数据建模的灵活基础表示法

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-01 DOI: 10.1016/j.spasta.2024.100841

Remy MacDonald, Benjamin Seiyon Lee

{"title":"Flexible basis representations for modeling large non-Gaussian spatial data","authors":"Remy MacDonald, Benjamin Seiyon Lee","doi":"10.1016/j.spasta.2024.100841","DOIUrl":"10.1016/j.spasta.2024.100841","url":null,"abstract":"<div><p>Nonstationary and non-Gaussian spatial data are common in various fields, including ecology (e.g., counts of animal species), epidemiology (e.g., disease incidence counts in susceptible regions), and environmental science (e.g., remotely-sensed satellite imagery). Due to modern data collection methods, the size of these datasets have grown considerably. Spatial generalized linear mixed models (SGLMMs) are a flexible class of models used to model nonstationary and non-Gaussian datasets. Despite their utility, SGLMMs can be computationally prohibitive for even moderately large datasets (e.g., 5000 to 100,000 observed locations). To circumvent this issue, past studies have embedded nested radial basis functions into the SGLMM. However, two crucial specifications (knot placement and bandwidth parameters), which directly affect model performance, are typically fixed prior to model-fitting. We propose a novel approach to model large nonstationary and non-Gaussian spatial datasets using adaptive radial basis functions. Our approach: (1) partitions the spatial domain into subregions; (2) employs reversible-jump Markov chain Monte Carlo (RJMCMC) to infer the number and location of the knots within each partition; and (3) models the latent spatial surface using partition-varying and adaptive basis functions. Through an extensive simulation study, we show that our approach provides more accurate predictions than competing methods while preserving computational efficiency. We demonstrate our approach on two environmental datasets - incidences of plant species and counts of bird species in the United States.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"62 ","pages":"Article 100841"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936200","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

Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods 具有计算密集型或棘手似然的空间过程的神经似然曲面

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-01 DOI: 10.1016/j.spasta.2024.100848

Julia Walchessen , Amanda Lenzi , Mikael Kuusela

{"title":"Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods","authors":"Julia Walchessen , Amanda Lenzi , Mikael Kuusela","doi":"10.1016/j.spasta.2024.100848","DOIUrl":"10.1016/j.spasta.2024.100848","url":null,"abstract":"<div><p>In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to real-world data because the likelihood function might be slow to evaluate or wholly intractable. In this work, we propose using convolutional neural networks to learn the likelihood function of a spatial process. Through a specifically designed classification task, our neural network implicitly learns the likelihood function, even in situations where the exact likelihood is not explicitly available. Once trained on the classification task, our neural network is calibrated using Platt scaling which improves the accuracy of the neural likelihood surfaces. To demonstrate our approach, we compare neural likelihood surfaces and the resulting maximum likelihood estimates and approximate confidence regions with the equivalent for exact or approximate likelihood for two different spatial processes—a Gaussian process and a Brown–Resnick process which have computationally intensive and intractable likelihoods, respectively. We conclude that our method provides fast and accurate parameter estimation with a reliable method of uncertainty quantification in situations where standard methods are either undesirably slow or inaccurate. The method is applicable to any spatial process on a grid from which fast simulations are available.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"62 ","pages":"Article 100848"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675324000393/pdfft?md5=23fbff43d8f95a95bb05cdb8ddb6dab6&pid=1-s2.0-S2211675324000393-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936255","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