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Multilevel Bivariate Areal Modelling for School Data: An Application with R-INLA
This paper studies the relationship between the territorial endowment of school infrastructure and services in Italy and the performance of high school students, expressed by the Invalsi scores for the second year of high school, by means of spatial ...
Leonardo Cefalo, Virgilio Gómez-Rubio
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Zoonoses and Public Health, 2022
AbstractHemorrhagic fever with renal syndrome (HFRS) is a category B infectious disease caused by Hantavirus infection, which can cause acute kidney injury and has a high mortality rate. At present, China is the country most severely afflicted by HFRS in the world, and it is critical to carry out efficient HFRS prevention and management in a scientific
Jiaqi, Teng +4 more
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AbstractHemorrhagic fever with renal syndrome (HFRS) is a category B infectious disease caused by Hantavirus infection, which can cause acute kidney injury and has a high mortality rate. At present, China is the country most severely afflicted by HFRS in the world, and it is critical to carry out efficient HFRS prevention and management in a scientific
Jiaqi, Teng +4 more
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Spatial Bayesian distributed lag non-linear models with R-INLA
International Journal of EpidemiologyM.Q.-Z. and J.B. gratefully acknowledge funding from the European Union's Horizon 2020 and Horizon Europe research and innovation programmes under grant agreement nos 865564 (European Research Council Consolidator Grant EARLY-ADAPT, https://www.early-adapt.eu/), 101069213 (European Research Council Proof-of-Concept HHS-EWS), and 101123382 ...
Marcos Quijal-Zamorano +3 more
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Seal encounters at sea: A contemporary spatial approach using R-INLA
Ecological Modelling, 2014Acoustic telemetry is an active field of research integral to the study of marine life. The latest generation of acoustic tags is making available new types of data. As part of the Ocean Tracking Network (OTN) (www.oceantrackingnework.org) acoustic tags known as VEMCO Mobile Transceivers (VMTs) (www.vemco.com) are being deployed on Sable Island grey ...
Stuart Carson, Joanna Mills Flemming
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Cross-covariance functions for additive and coupled joint spatiotemporal SPDE models in R-INLA
Environmental and Ecological Statistics, 2017Models for multivariate space–time geostatistical data have received a growing interest in spatial and spatiotemporal epidemiology. However, specifying models that can capture associations within and among multivariate measurements is usually a challenge. The main goal of this paper is to introduce and review cross-covariance functions that are rich in
Kifle, Yimer Wasihun +2 more
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Geoderma, 2016
Abstract As any model for digital soil mapping suffers from different types of errors, including interpolation errors, it is important to quantify the uncertainty associated with the maps produced. The most common approach is some form of regression kriging or variation involving geostatistical simulation.
Poggio L, Gimona A, Spezia L, Brewer MJ
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Abstract As any model for digital soil mapping suffers from different types of errors, including interpolation errors, it is important to quantify the uncertainty associated with the maps produced. The most common approach is some form of regression kriging or variation involving geostatistical simulation.
Poggio L, Gimona A, Spezia L, Brewer MJ
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Spatial and Spatio-temporal Epidemiology, 2013
The publisher regrets that the printed version of the above article contained a number of errors. The correct and final version follows. The publisher would like to apologise for any inconvenience caused.
Blangiardo, Marta +3 more
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The publisher regrets that the printed version of the above article contained a number of errors. The correct and final version follows. The publisher would like to apologise for any inconvenience caused.
Blangiardo, Marta +3 more
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2018
Spatio-temporal data containing information about space, time and its interaction allows researchers to describe potential geographical pattern. Bayesian method commonly used in describing spatio-temporal data is simulated based Markov Chain Monte Carlo (MCMC).
Ro +3 more
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Spatio-temporal data containing information about space, time and its interaction allows researchers to describe potential geographical pattern. Bayesian method commonly used in describing spatio-temporal data is simulated based Markov Chain Monte Carlo (MCMC).
Ro +3 more
openaire +1 more source