24 papers · ranked by Valyu relevance
Naeimehossadat Asmarian, Seyyed Mohammad Taghi Ayatollahi, Zahra Sharafi, Najaf Zare
'Zahra Sharafi' 'Najaf Zare'] Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This…
Isa Marques, Paul F. V. Wiemann
A common phenomenon in spatial regression models is spatial confounding. This phenomenon occurs when spatially indexed covariates modeling the mean of the response are correlated with a spatial effect included in the model. spatial+ (Dupont et al., 2022) is a popular approach to reducing spatial confounding. spatial+…
Andrew O. Finley, Sudipto Banerjee, Alan E. Gelfand
In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on improving computational efficiency, flexibility, and usability for point-referenced data models. Attention is given to algorithm and computing developments that result in improved sampler…
Sudipto Banerjee
Geographic Information Systems (GIS) and related technologies have generated substantial interest among statisticians with regard to scalable methodologies for analyzing large spatial datasets. A variety of scalable spatial process models have been proposed that can be easily embedded within a hierarchical modeling…
Joel Eliason, Michele Peruzzi, Arvind Rao
Spatial dependencies in tissue microenvironments, particularly asymmetric interactions between cell types, are central to understanding immune dynamics, tumor behavior, and tissue organization. Existing spatial statistical methods often assume symmetric associations or analyze images independently, limiting biological…
Francisco Louzada, Diego C. Nascimento, Osafu Augustine Egbon
Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data…
Lu Zhang, Sudipto Banerjee, Andrew O. Finley
Joint modeling of spatially oriented dependent variables is commonplace in the environmental sciences, where scientists seek to estimate the relationships among a set of environmental outcomes accounting for dependence among these outcomes and the spatial dependence for each outcome. Such modeling is now sought for…
Carter Allen, Yuzhou Chang, Brian Neelon, Won Chang + 4 more
High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek…
Anna Gloria Billé, Giuseppe Arbia
Despite spatial econometrics is now considered a consolidated discipline, only in recent years we have experienced an increasing attention to the possibility of applying it to the field of discrete choices (e.g. Smirnov, 2010 for a recent review) and limited dependent variable models. In particular, only a small number…
David Abramian, Per Sidén, Hans Knutsson, Mattias Villani + 1 more
Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field…
Ali Arab, Igor Burstyn, Gheorghe Luta
Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for modeling data with…
Miriam Marco, Enrique Gracia, Antonio López-Quílez, Marisol Lila + 1 more
'Carol Cunradi'] Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors, with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We…
Duncan Lee
Disease maps display the spatial pattern in disease risk, so that highrisk clusters can be identified. The spatial structure in the risk map is typically represented by a set of random effects, which are modelled with a conditional autoregressive (CAR) prior. Such priors include a global spatial smoothing parameter…
Ting Liu, Dengke Xu, Shiqi Ke, Ricardo Sandes Ehlers
Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance…
Jungin Choi, Abhirup Datta, Martin A. Lindquist
Task-based fMRI is commonly analyzed using voxel-wise general linear models, a non-spatial scalable approach that can yield fragmented activation maps. Spatial alternatives such as kernel smoothing and Bayesian models address this but either blur activation boundaries or are computationally prohibitive at modern…
Marcos O. Prates, Érica Castilho Rodrigues, Renato Assunção
Spatial confounding between the spatial random effects and fixed effects covariates has been recently discovered and showed that it may bring misleading interpretation to the model results. Solutions to alleviate this problem are based on decomposing the spatial random effect and fitting a restricted spatial…
Michael Dumelle, Matt Higham, Jay M. Ver Hoef, A. K. M. Anisur Rahman
'A. K. M. Anisur Rahman'] spmodel is an R package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms.…
Joseph M. Northrup, Brian D. Gerber
Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent…
Clark S. Rushing, J. Andrew Royle, David J. Ziolkowski, Keith L. Pardieck
Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range…
Kok Ben Toh, Denis Valle
Geospatial statistical models play an important role in malaria control and prevention; they are widely used to produce malaria risk maps, which are essential to guide efficient resource allocation for intervention. Although many models are available for spatial mapping, the most commonly used model in the literature…
Inesh Munaweera, Les N. Harris, Jean-Sébastien Moore, Ross F. Tallman + 6 more
Hierarchical modelling is frequently used to model ecological processes because of its ability to handle complex ecological phenomena by decomposing them into naturally explainable sub-models. Hierarchical Bayesian approaches have gained widespread use in health, social, and environmental sciences, including in the…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Authors not listed
Atmospheric dispersion models are a key component for characterizing methane emissions on oil and gas sites. While some model implementations with varying degrees of complexity are available, existing regulatory-grade dispersion models are cumbersome to apply within an inversion framework on a routine operational level…
Mei Tessum, Susan Anenberg, Zoe Chafe, Daven Henze + 4 more
To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside…