Spatial Statistics最新文献

英文中文

Spatial empirical best predictor of small area linear parameter for positively skewed outcomes 空间经验是小面积线性参数对正偏倚结果的最佳预测因子

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-11-04 DOI: 10.1016/j.spasta.2025.100941

Dian Handayani , Khairil Anwar Notodiputro , Asep Saefuddin , I Wayan Mangku , Anang Kurnia

Small area estimation (SAE) addresses the estimation of parameters for population subsets when the sample itself is too small to produce reliable direct estimates. The standard method, empirical best linear unbiased prediction, uses a predictor under a linear mixed model that assumes normality of the variable of interest and independence among small areas. However, in practical studies, the distribution of the variable of interest tends to be positively skewed and there exists spatial dependence among the small areas. To address both of these, a previous study had proposed a spatial synthetic (SYNT) predictor that predicts non-sampled values of the variable of interest using its unconditional mean. The SYNT predictor is derived based on a unit-level spatial lognormal mixed model. Herein, we propose spatial empirical best predictor (SEBP) to improve the SYNT predictor by using its conditional mean to predict the non-sampled values of the variable of interest. We perform simulation studies to evaluate the performance of SEBP and compare them with those of the SYNT predictor and other existing methods. Our results reveal that the SEBP performs better in terms of the average relative bias and average relative root mean square error when the spatial correlation among small areas is small, medium or large. In an SAE application on the average monthly household per-capita expenditure for sub-districts in Bogor, Indonesia, the proposed SEBP provides better estimates than other established methods.

当样本本身太小而无法产生可靠的直接估计时，小面积估计（SAE）解决了总体子集参数的估计。标准方法，经验最佳线性无偏预测，使用线性混合模型下的预测器，该模型假设感兴趣变量的正态性和小区域之间的独立性。但在实际研究中，兴趣变量的分布趋于正偏，小区域间存在空间依赖性。为了解决这两个问题，之前的一项研究提出了一个空间合成（SYNT）预测器，该预测器使用目标变量的无条件平均值来预测其非采样值。SYNT预测器是基于单位级空间对数正态混合模型导出的。在此，我们提出空间经验最佳预测器（SEBP）来改进SYNT预测器，通过使用其条件均值来预测感兴趣变量的非采样值。我们进行了模拟研究来评估SEBP的性能，并将其与SYNT预测器和其他现有方法进行了比较。结果表明，当小区域间的空间相关性为小、中、大时，SEBP在平均相对偏差和平均相对均方根误差方面表现较好。在一项SAE关于印度尼西亚茂物街道平均每月家庭人均支出的应用中，拟议的SEBP提供了比其他现有方法更好的估计。

{"title":"Spatial empirical best predictor of small area linear parameter for positively skewed outcomes","authors":"Dian Handayani , Khairil Anwar Notodiputro , Asep Saefuddin , I Wayan Mangku , Anang Kurnia","doi":"10.1016/j.spasta.2025.100941","DOIUrl":"10.1016/j.spasta.2025.100941","url":null,"abstract":"<div><div>Small area estimation (SAE) addresses the estimation of parameters for population subsets when the sample itself is too small to produce reliable direct estimates. The standard method, empirical best linear unbiased prediction, uses a predictor under a linear mixed model that assumes normality of the variable of interest and independence among small areas. However, in practical studies, the distribution of the variable of interest tends to be positively skewed and there exists spatial dependence among the small areas. To address both of these, a previous study had proposed a spatial synthetic (SYNT) predictor that predicts non-sampled values of the variable of interest using its unconditional mean. The SYNT predictor is derived based on a unit-level spatial lognormal mixed model. Herein, we propose spatial empirical best predictor (SEBP) to improve the SYNT predictor by using its conditional mean to predict the non-sampled values of the variable of interest. We perform simulation studies to evaluate the performance of SEBP and compare them with those of the SYNT predictor and other existing methods. Our results reveal that the SEBP performs better in terms of the average relative bias and average relative root mean square error when the spatial correlation among small areas is small, medium or large. In an SAE application on the average monthly household per-capita expenditure for sub-districts in Bogor, Indonesia, the proposed SEBP provides better estimates than other established methods.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100941"},"PeriodicalIF":2.5,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528797","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

Dynamic spatial regimes for spatial panel data 空间面板数据的动态空间机制

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-11-01 DOI: 10.1016/j.spasta.2025.100939

Anna Gloria Billé , Roberto Benedetti , Paolo Postiglione

Spatial heterogeneity in terms of spatially-varying coefficients is often not properly considered in modeling economic data. This neglect might cause serious problems in the estimation of the parameters of a model specification when group-wise heterogeneity is at work. In this paper we propose a two-step algorithm for the identification of endogenous (data-driven) spatial regimes by using an iterative procedure that is based on weighting functions updated dynamically over time. In the first step, clusters of spatial units (i.e. spatial regimes) are defined using both space and time information. In the second step, a spatial panel data model with random effects is estimated with the spatial regimes identified in the previous step. The additional random effects assumption on the model specification ensures the possibility of controlling also for individual effects as well as group-wise slope coefficients. The proposed method is applied to two real data sets to illustrate our procedure.

在经济数据建模中，往往没有适当地考虑空间变化系数的空间异质性。当群体异质性在起作用时，这种忽视可能会导致模型规范参数估计中的严重问题。在本文中，我们提出了一种两步算法，通过使用基于随时间动态更新的权重函数的迭代过程来识别内源性（数据驱动）空间制度。在第一步中，使用空间和时间信息定义空间单元簇（即空间状态）。在第二步中，利用前一步中识别的空间状态估计具有随机效应的空间面板数据模型。模型规范上附加的随机效应假设确保了控制个体效应和群体斜率系数的可能性。将该方法应用于两个实际数据集来说明我们的方法。

引用次数: 0

Growth of spatial statistics for agriculture and environment: The example of BioSP at INRAE 农业与环境空间统计的增长：以印度农业与环境研究所的BioSP为例

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 DOI: 10.1016/j.spasta.2025.100938

Denis Allard

This paper illustrates how progress in spatial statistics is fueled by scientific questions arising from applications in agriculture and environment. The unifying theme is the work that has been carried out at BioSP, a statistics and mathematics research unit mainly affiliated to the “Mathematics and Digital Technologies” division at INRAE, the French National Research Institute for Agriculture, Food and Environment. Starting from the 20 contributions that BioSP members have published in Spatial Statistics since its creation in 2012, almost fifteen years of advances are reviewed, spanning point processes, (multivariate) spatio-temporal Gaussian processes, compositional data, stochastic weather generators and extreme value theory. Most of the content is focused on theoretical and methodological developments, with examples being limited due to length constraints for the article. Attention is given to how these advances have been inspired by problems arising in other research domains. In return, it will be shown how they have opened new research questions in spatial statistics and how they had impact in the scientific fields they originated from. In conclusion, some perspectives and outlooks are discussed, in particular in relation to the AI revolution.

本文阐述了空间统计的进步是如何由农业和环境应用中产生的科学问题推动的。统一的主题是在BioSP进行的工作，BioSP是一个统计和数学研究单位，主要隶属于法国国家农业、食品和环境研究所（INRAE）的“数学和数字技术”部门。自2012年创立以来，BioSP成员在《空间统计学》上发表了20篇文章，回顾了近15年来的进展，包括点过程、（多元）时空高斯过程、成分数据、随机天气发生器和极值理论。大部分内容集中在理论和方法的发展，由于文章的长度限制，示例有限。关注这些进步是如何受到其他研究领域中出现的问题的启发的。作为回报，它将展示他们如何在空间统计中开辟了新的研究问题，以及他们如何在他们所起源于的科学领域产生影响。最后，讨论了一些观点和展望，特别是与人工智能革命有关的观点和展望。

{"title":"Growth of spatial statistics for agriculture and environment: The example of BioSP at INRAE","authors":"Denis Allard","doi":"10.1016/j.spasta.2025.100938","DOIUrl":"10.1016/j.spasta.2025.100938","url":null,"abstract":"<div><div>This paper illustrates how progress in spatial statistics is fueled by scientific questions arising from applications in agriculture and environment. The unifying theme is the work that has been carried out at BioSP, a statistics and mathematics research unit mainly affiliated to the “Mathematics and Digital Technologies” division at INRAE, the French National Research Institute for Agriculture, Food and Environment. Starting from the 20 contributions that BioSP members have published in <em>Spatial Statistics</em> since its creation in 2012, almost fifteen years of advances are reviewed, spanning point processes, (multivariate) spatio-temporal Gaussian processes, compositional data, stochastic weather generators and extreme value theory. Most of the content is focused on theoretical and methodological developments, with examples being limited due to length constraints for the article. Attention is given to how these advances have been inspired by problems arising in other research domains. In return, it will be shown how they have opened new research questions in spatial statistics and how they had impact in the scientific fields they originated from. In conclusion, some perspectives and outlooks are discussed, in particular in relation to the AI revolution.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100938"},"PeriodicalIF":2.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269553","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 concordance coefficient for lattice data: An application to poverty indices in Chile 格点数据的一致性系数：在智利贫困指数中的应用

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-27 DOI: 10.1016/j.spasta.2025.100936

Ronny Vallejos , Clemente Ferrer , Jorge Mateu

This paper introduces a novel coefficient for measuring agreement between two lattice sequences observed in the same areal units, motivated by the analysis of different methodologies for measuring poverty rates in Chile. Building on the multivariate concordance coefficient framework, our approach accounts for dependencies in the multivariate lattice process using a non-negative definite matrix of weights, assuming a Multivariate Conditionally Autoregressive (GMCAR) process. We adopt a Bayesian perspective for inference, using summaries from Bayesian estimates. The methodology is illustrated through an analysis of poverty rates in the Metropolitan and Valparaíso regions of Chile, with High Posterior Density (HPD) intervals provided for the poverty rates. This work addresses a methodological gap in the understanding of agreement coefficients and enhances the usability of these measures in the context of social variables typically assessed in areal units.

本文介绍了一种新的系数，用于测量在同一面积单位中观察到的两个晶格序列之间的一致性，其动机是对智利测量贫困率的不同方法的分析。在多元一致性系数框架的基础上，我们的方法使用非负确定的权重矩阵来解释多元格过程中的依赖关系，假设一个多元条件自回归（GMCAR）过程。我们采用贝叶斯的观点进行推理，使用贝叶斯估计的总结。该方法通过对智利大都市和Valparaíso地区贫困率的分析来说明，并为贫困率提供了高后验密度（HPD）间隔。这项工作解决了在理解协议系数方面的方法差距，并提高了这些措施在通常以面积单位评估的社会变量背景下的可用性。

引用次数: 0

Navigating challenges in spatio-temporal modelling of Antarctic krill abundance: Addressing zero-inflated data and misaligned covariates 南极磷虾丰度时空建模中的导航挑战：解决零膨胀数据和错位协变量

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-26 DOI: 10.1016/j.spasta.2025.100937

André Victor Ribeiro Amaral , Adam M. Sykulski , Sophie Fielding , Emma Cavan

Antarctic krill (Euphausia superba) are among the most abundant species on our planet and serve as a vital food source for many marine predators in the Southern Ocean. In this paper, we utilise statistical spatio-temporal methods to combine data from various sources and resolutions, aiming to model krill abundance. Our focus lies in fitting the model to a dataset comprising acoustic measurements of krill biomass. To achieve this, we integrate climate covariates obtained from satellite imagery and from drifting surface buoys (also known as drifters). Additionally, we use sparsely collected krill biomass data obtained from net fishing efforts (KRILLBASE) for validation. However, integrating these multiple heterogeneous data sources presents significant modelling challenges, including spatio-temporal misalignment and inflated zeros in the observed data. To address these challenges, we fit a Hurdle-Gamma model to jointly describe the occurrence of zeros and the krill biomass for the non-zero observations, while also accounting for misaligned and heterogeneous data sources, including drifters. Therefore, our work presents a comprehensive framework for analysing and predicting krill abundance in the Southern Ocean, leveraging information from various sources and formats. This is crucial due to the impact of krill fishing, as understanding their distribution is essential for informed management decisions and fishing regulations aimed at protecting the species.

南极磷虾（Euphausia superba）是地球上最丰富的物种之一，是南大洋许多海洋捕食者的重要食物来源。在本文中，我们利用统计时空的方法来结合来自不同来源和分辨率的数据，旨在模拟磷虾丰度。我们的重点在于将模型拟合到包含磷虾生物量声学测量的数据集。为了实现这一目标，我们整合了从卫星图像和漂流水面浮标（也称为漂流浮标）获得的气候协变量。此外，我们使用从网捕捞努力中获得的稀疏收集的磷虾生物量数据（KRILLBASE）进行验证。然而，整合这些多个异构数据源带来了重大的建模挑战，包括观测数据中的时空错位和虚零。为了解决这些挑战，我们拟合了一个障碍-伽玛模型来共同描述非零观测值的零和磷虾生物量的发生，同时也考虑了不对齐和异构数据源，包括漂移。因此，我们的工作提出了一个综合框架，用于分析和预测南大洋磷虾丰度，利用各种来源和格式的信息。由于磷虾捕捞的影响，这一点至关重要，因为了解磷虾的分布对于明智的管理决策和旨在保护该物种的捕捞法规至关重要。

{"title":"Navigating challenges in spatio-temporal modelling of Antarctic krill abundance: Addressing zero-inflated data and misaligned covariates","authors":"André Victor Ribeiro Amaral , Adam M. Sykulski , Sophie Fielding , Emma Cavan","doi":"10.1016/j.spasta.2025.100937","DOIUrl":"10.1016/j.spasta.2025.100937","url":null,"abstract":"<div><div>Antarctic krill (<em>Euphausia superba</em>) are among the most abundant species on our planet and serve as a vital food source for many marine predators in the Southern Ocean. In this paper, we utilise statistical spatio-temporal methods to combine data from various sources and resolutions, aiming to model krill abundance. Our focus lies in fitting the model to a dataset comprising acoustic measurements of krill biomass. To achieve this, we integrate climate covariates obtained from satellite imagery and from drifting surface buoys (also known as drifters). Additionally, we use sparsely collected krill biomass data obtained from net fishing efforts (KRILLBASE) for validation. However, integrating these multiple heterogeneous data sources presents significant modelling challenges, including spatio-temporal misalignment and inflated zeros in the observed data. To address these challenges, we fit a Hurdle-Gamma model to jointly describe the occurrence of zeros and the krill biomass for the non-zero observations, while also accounting for misaligned and heterogeneous data sources, including drifters. Therefore, our work presents a comprehensive framework for analysing and predicting krill abundance in the Southern Ocean, leveraging information from various sources and formats. This is crucial due to the impact of krill fishing, as understanding their distribution is essential for informed management decisions and fishing regulations aimed at protecting the species.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100937"},"PeriodicalIF":2.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222039","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 framework for analysing point patterns on nonconvex domains using visibility graphs and multidimensional scaling 一个使用可见性图和多维尺度分析非凸域上点模式的框架

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-25 DOI: 10.1016/j.spasta.2025.100935

Kabelo Mahloromela, Inger Fabris-Rotelli

A point pattern is typically analysed to understand the first- and second-order properties of the underlying point process. These properties are usually inferred using estimation procedures that depend on interpoint distance and are thus sensitive to the choice of distance metric. Euclidean distance is conventionally used to quantify proximity between points, but it does not accurately reflect spatial relationships when points are constrained within irregular, nonconvex spatial domains. Herein, we propose a strategy to embed visibility graph distances into Euclidean metric space using multidimensional scaling. The aim is to simplify analyses, leverage well-developed methods based on Euclidean distance, and retain, as far as possible, the true proximity relationships on a nonconvex spatial domain. The kernel smoothed intensity estimate and the

K

-function are computed in this new spatial context and used to validate the effectiveness of the embedding strategy.

分析点模式通常是为了了解底层点过程的一阶和二阶性质。这些属性通常使用依赖于点间距离的估计过程来推断，因此对距离度量的选择很敏感。欧几里得距离通常用于量化点之间的接近程度，但是当点被约束在不规则的非凸空间域中时，它不能准确地反映空间关系。本文提出了一种利用多维尺度将可见性图距离嵌入欧氏度量空间的策略。其目的是简化分析，利用基于欧几里得距离的成熟方法，并尽可能地保留非凸空间域上的真正接近关系。在新的空间环境下计算核平滑强度估计和k函数，并用于验证嵌入策略的有效性。

引用次数: 0

Bandwidth selection for the intensity in spatial point processes 空间点过程中强度的带宽选择

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-25 DOI: 10.1016/j.spasta.2025.100928

Yangha Chung , Ji Meng Loh , Woncheol Jang

We introduce a doubly-smoothed bandwidth selection method to obtain bandwidth matrices

H

for estimating the intensity function of a spatial point process. The doubly-smoothed bootstrap involves taking bootstrap samples by adding random noise and using Dirichlet rather than multinomial weights. The mean integrated squared error (MISE) and asymptotic mean integrated squared error (AMISE) as a function of

H

can then be computed numerically using the bootstrap samples, with optimal

H

obtained by minimizing the MISE or AMISE with respect to

H .

We present simulation results comparing the doubly-smoothed bandwidth selection method with other methods for a number of intensity functions. We also apply our methods to a data set of police pedestrian stops in New York City.

提出了一种双平滑带宽选择方法，用于估计空间点过程的强度函数，从而得到带宽矩阵H。双平滑自举包括通过添加随机噪声和使用狄利克雷而不是多项权重来获取自举样本。然后可以使用自举样本数值计算平均积分平方误差（MISE）和渐近平均积分平方误差（AMISE）作为H的函数，通过最小化MISE或AMISE相对于H获得最优H。我们给出了将双平滑带宽选择方法与许多强度函数的其他方法进行比较的仿真结果。我们还将我们的方法应用于纽约市警察行人站的数据集。

引用次数: 0

Spatial and spatio-temporal cluster detection using stacking 基于叠加的时空聚类检测

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-24 DOI: 10.1016/j.spasta.2025.100933

Maria E. Kamenetsky , Jun Zhu , Ronald E. Gangnon

Patterns in disease across space and time are important to epidemiologists and health professionals because they may indicate underlying elevated disease risk. In some cases, elevated risk may be driven by environmental exposures, infectious diseases or other factors where timely public health interventions are important. The spatial and spatio-temporal scan statistics identify a single most likely cluster or equivalently select a single correct model. We instead consider an ensemble of single cluster models. We use stacking, a model-averaging technique, to combine relative risk estimates from all of the single cluster models into a sequence of meta-models indexed by the effective number of parameters/clusters. The number of parameters/spatio-temporal clusters is chosen using information criteria. A simulation study is conducted to demonstrate the statistical properties of the stacking method. The method is illustrated using a dataset of female breast cancer incidence data at the municipality level in Japan.

跨空间和时间的疾病模式对流行病学家和卫生专业人员很重要，因为它们可能表明潜在的疾病风险升高。在某些情况下，风险升高可能是由环境暴露、传染病或及时的公共卫生干预很重要的其他因素造成的。空间和时空扫描统计信息确定一个最可能的集群或选择一个正确的模型。我们转而考虑单个集群模型的集合。我们使用叠加，一种模型平均技术，将所有单个聚类模型的相对风险估计合并到由参数/聚类的有效数量索引的元模型序列中。使用信息标准选择参数/时空聚类的数量。通过仿真研究验证了该方法的统计特性。该方法使用日本市级女性乳腺癌发病率数据集进行说明。

引用次数: 0

Specifying spatial effects in panel data: Locally robust vs. conditional tests 指定面板数据中的空间效果：局部鲁棒测试与条件测试

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-23 DOI: 10.1016/j.spasta.2025.100934

Giovanni Millo

We address the issue of specifying a spatial lag vs. spatial error process in spatial panel models. The popular locally robust Lagrange multiplier (RLM) tests for spatial lag vs. error are compared to optimal alternatives based on maximum likelihood estimation: Wald and likelihood ratio (LR) tests requiring estimation of the full encompassing model, and conditional Lagrange multiplier (CLM) tests drawing on the reduced specification. Monte Carlo simulations are performed in a typical spatial panel context. Individual effects are successfully eliminated through the forward orthogonal deviations transformation, making the RLM suitable for panel data. Nevertheless, the statistical properties of Wald and LR are superior to those of the RLM. The CLM also dominates the RLM, as long as the sample is at least of moderate size. The RLM are computationally very convenient, but ML-based tests are feasible in most usage cases on mainstream hardware.

我们解决了在空间面板模型中指定空间滞后与空间误差过程的问题。将流行的局部鲁棒拉格朗日乘数（RLM）空间滞后与误差测试与基于最大似然估计的最佳替代方案进行比较：Wald和似然比（LR）测试需要估计完整的包含模型，以及基于简化规范的条件拉格朗日乘数（CLM）测试。蒙特卡罗模拟是在典型的空间面板环境中进行的。通过前向正交偏差变换，成功地消除了个体影响，使RLM适用于面板数据。然而，Wald和LR的统计性质优于RLM。只要样本至少是中等大小，CLM也支配着RLM。RLM在计算上非常方便，但是基于ml的测试在主流硬件上的大多数使用情况下是可行的。

引用次数: 0

Model averaging for spatial autoregressive panel data models 空间自回归面板数据模型的模型平均

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-23 DOI: 10.1016/j.spasta.2025.100931

Aibing Ji, Jingxuan Li, Qingqing Li

The spatial autoregressive panel data models are widely employed in regional economics to capture spatial dependencies, but conventional specifications rely on a single spatial weight matrix, heightening the risk of model misspecification. Current research lacks systematic model averaging methods for integrating multiple weight matrices and addressing spatial effect uncertainty. This study proposes a novel model averaging framework for spatial autoregressive panel data models with fixed effects, extending model averaging methodology to the spatial panel context and enabling flexible combinations of multiple weight matrices for both dependent variables and error terms. An adaptive Mallows-type criterion is developed, dynamically adjusting to the presence or absence of spatial effects, with its asymptotic optimality established. Monte Carlo simulations confirm robustness across scenarios with no, single, or mixed spatial dependencies. An empirical application to Chinese provincial housing prices identifies economic adjacency as the key spatial dependence driver, validating the method’s predictive accuracy and policy utility for spatiotemporal data analysis.

空间自回归面板数据模型在区域经济学中被广泛应用于捕获空间依赖关系，但传统的规范依赖于单一的空间权重矩阵，增加了模型错误规范的风险。目前的研究缺乏系统的模型平均方法来积分多个权重矩阵和处理空间效应的不确定性。本研究为具有固定效应的空间自回归面板数据模型提出了一种新的模型平均框架，将模型平均方法扩展到空间面板环境，并实现了因变量和误差项的多个权重矩阵的灵活组合。提出了一种自适应mallows型准则，根据空间效应的存在或不存在进行动态调整，并建立了其渐近最优性。蒙特卡罗模拟证实了在没有、单一或混合空间依赖性的情况下的鲁棒性。通过对中国省级房价的实证分析，发现经济邻接性是主要的空间依赖驱动因素，验证了该方法在时空数据分析中的预测准确性和政策实用性。

{"title":"Model averaging for spatial autoregressive panel data models","authors":"Aibing Ji, Jingxuan Li, Qingqing Li","doi":"10.1016/j.spasta.2025.100931","DOIUrl":"10.1016/j.spasta.2025.100931","url":null,"abstract":"<div><div>The spatial autoregressive panel data models are widely employed in regional economics to capture spatial dependencies, but conventional specifications rely on a single spatial weight matrix, heightening the risk of model misspecification. Current research lacks systematic model averaging methods for integrating multiple weight matrices and addressing spatial effect uncertainty. This study proposes a novel model averaging framework for spatial autoregressive panel data models with fixed effects, extending model averaging methodology to the spatial panel context and enabling flexible combinations of multiple weight matrices for both dependent variables and error terms. An adaptive Mallows-type criterion is developed, dynamically adjusting to the presence or absence of spatial effects, with its asymptotic optimality established. Monte Carlo simulations confirm robustness across scenarios with no, single, or mixed spatial dependencies. An empirical application to Chinese provincial housing prices identifies economic adjacency as the key spatial dependence driver, validating the method’s predictive accuracy and policy utility for spatiotemporal data analysis.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100931"},"PeriodicalIF":2.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159566","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

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Spatial Statistics

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀