Results 61 to 70 of about 3,465 (151)

A Bayesian Spatiotemporal Functional Model for Data With Block Structure and Repeated Measures

Environmetrics, Volume 37, Issue 2, March 2026.
ABSTRACT The analysis of spatiotemporal data is fundamental across multiple scientific disciplines, particularly in assessing the behavior of climate effects over space and time. A key challenge in this area is effectively capturing recurring climate phenomena, such as El Niño/La Niña (ENSO) phases, which induce prolonged periods of similar weather ...
David H. da Matta, Mariana R. Motta, Nancy L. Garcia, Alexandre B. Heinemann   +3 more
wiley   +1 more source

Bayesian spatial prediction of soil organic carbon stocks in eastern DRC using INLA-SPDE and environmental covariates

Environmental Challenges
Soil organic carbon (SOC) plays a critical role in climate mitigation and agricultural sustainability, yet its spatial distribution in the eastern Democratic Republic of the Congo (DRC) remains poorly quantified.
Alain Matazi Kangela, Bitaisha Nakishuka Shukuru, Serge Mugisho Mukotanyi, Gerard Imani, Yannick Mugumaarhama, Daniel Muhindo Iragi, Dieudonné Shamamba Bahati, Janvier Bigabwa Bashagaluke, Wivine Munyahali   +8 more
doaj   +1 more source

Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes

Ecosphere, 2017
Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City.
Mark H. Myer, Scott R. Campbell, John M. Johnston   +2 more
doaj   +1 more source

Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER data

BMC Cancer, 2021
Background Prostate cancer (CaP) cases are high in the United States. According to the American Cancer Society, there are an estimated number of 174,650 CaP new cases in 2019.
Justice Moses K. Aheto, Ovie A. Utuama, Getachew A. Dagne   +2 more
doaj   +1 more source

On the choice of the mesh for the analysis of geostatistical data using R-INLA

Communications in Statistics - Theory and Methods, 2018
Many methods used in spatial statistics are computationally demanding, and so, the development of more computationally efficient methods has received attention. A important development is the integrated nested Laplace approximation method which is carry out Bayesian analysis more efficiently This method, for geostatistical data, is done considering the
Ana Julia Righetto, Christel Faes, Yannick Vandendijck, Paulo Justiniano Ribeiro   +3 more
openaire   +2 more sources

Bayesian Inference for Spatially‐Temporally Misaligned Data Using Predictive Stacking

Environmetrics, Volume 37, Issue 2, March 2026.
ABSTRACT Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly
Soumyakanti Pan, Sudipto Banerjee
wiley   +1 more source

Coherent Disaggregation and Uncertainty Quantification for Spatially Misaligned Data

Environmetrics, Volume 37, Issue 2, March 2026.
ABSTRACT Spatial misalignment arises when datasets are aggregated or collected at different spatial scales, leading to information loss. We develop a Bayesian disaggregation framework that links misaligned data to a continuous‐domain model through an iteratively linearised integration scheme implemented with the Integrated Nested Laplace Approximation (
Man Ho Suen, Mark Naylor, Finn Lindgren
wiley   +1 more source

Model‐Based Data Integration Improves Species Distribution Models for Data‐Deficient and Narrow‐Ranged Hummingbird Species

Diversity and Distributions, Volume 32, Issue 3, March 2026.
ABSTRACT Aim For species with narrow ranges or low population sizes, a deficiency of species occurrence records can limit the capacity to build accurate species distribution models (SDMs). Model‐based integration of data from multiple sources has been offered as a solution to improve predictions of species' distributions at large scales, especially for
Jussi Mäkinen, Jeremy Cohen, Walter Jetz   +2 more
wiley   +1 more source

A standardization procedure to incorporate variance partitioning‐based priors in latent Gaussian models

Scandinavian Journal of Statistics, Volume 53, Issue 1, Page 364-394, March 2026.
ABSTRACT Latent Gaussian models (LGMs) are a subset of Bayesian Hierarchical models where Gaussian priors, conditional on variance parameters, are assigned to all effects in the model. LGMs are employed in many fields for their flexibility and computational efficiency. However, practitioners find prior elicitation on the variance parameters challenging
Luisa Ferrari, Massimo Ventrucci
wiley   +1 more source

Regional ecosystem responses to environmental drivers in cold‐water lakes evaluated using stable isotopes of salmonid fishes

Limnology and Oceanography, Volume 71, Issue 2, Page 1-14, February 2026.
Abstract Diverse environmental drivers influence food web energy and nutrient flows, a key ecosystem function, yet their relative importance remains poorly known. We compiled thousands of bulk stable isotope measurements (carbon and nitrogen) of brown trout (Salmo trutta, n = 2854) and Arctic charr (Salvelinus alpinus, n = 3062) from 120 cold‐water ...
Matthew R. D. Cobain, Per‐Arne Amundsen, Åge Brabrand, Pär Byström, Jan G. Davidsen, Karl Ø. Gjelland, Jennifer E. Hansen, Ingeborg P. Helland, Trygve Hesthagen, Stein I. Johnsen, Roger I. Jones, Kimmo K. Kahilainen, Henna Kangosjärvi, Gaute Kjærstad, Rune Knudsen, Hanna‐Kaisa Lakka, Jon Museth, Michael Power, Sigurd Rognerud, Javier Sánchez‐Hernández, Odd Terje Sandlund, Antti P. Eloranta   +21 more
wiley   +1 more source