Results 91 to 100 of about 15,041,214 (186)

US county-level food insecurity and COVID-19 mortality: a Bayesian spatial analysis with R-INLA

American Journal of Epidemiology
Abstract The objective of our study was to evaluate the relationship between county-level food insecurity (FI) and the risk of Coronavirus-2019 (COVID-19) mortality in the United States during the early pandemic (March 25, 2020-December 25, 2021), while accounting for geographic contributions to that risk.
Christian A Maino Vieytes, Mauricio Campos, Sara L McLafferty, May A Beydoun, Francesca Gany, Anna E Arthur, Rebecca L Smith   +6 more
openaire   +2 more sources

Aproximações de distribuições marginais a posteriori utilizando o método INLA [PDF]

, 2013
O uso da Estatística Bayesiana vem se tornando cada vez mais frequente, isso pois os modelos são mais intuitivos e em alguns casos apenas modelos Bayesianos obtêm sucesso (modelos de Teoria da Resposta ao Item por exemplo). O grande problema surge quando
Azevedo, Douglas Roberto Mesquita
core   +1 more source

Desempeño predictivo de R-INLA SPDE para el Mapeo Digital de Suelos

, 2021
El mapeo digital de suelos (MDS) permite describir la variabilidad espacial de una propiedad edáfica a treves de modelos de predicción espacial que explican la relación que existe entre la variable de interés y covariables sitio-especificas. Entre los modelos estadísticos más incipientes en aplicaciones de MDS está la regresión bayesiana ajustada con ...
Giannini Kurina, Franca, Suarez, Franco Marcelo, Paccioretti, Pablo Ariel, Macchiavelli, Raul, Balzarini, Monica Graciela   +4 more
openaire   +1 more source

Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia

BMC Public Health
Introduction Dengue is a mosquito-borne disease caused by the dengue virus, primarily transmitted by Aedes aegypti and Aedes albopictus. Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction
Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono   +2 more
doaj   +1 more source

sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields

Journal of Statistical Software
Geostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally ...
Sean C. Anderson, Eric J. Ward, Philina A. English, Lewis A. K. Barnett, James T. Thorson   +4 more
doaj   +1 more source

Spatio-temporal characteristics of Tuberculosis in Ghana [version 3; peer review: 1 approved, 2 approved with reservations]

F1000Research
Background Tuberculosis (TB) continues to be a prominent contributor to global mortality, standing as the second most fatal infectious disease and holding the seventh position among the top ten causes of death in Ghana.
Francis Kwame Bukari, Emmanuel A. Amikiya, Abdul-Karim Iddrisu   +2 more
doaj   +1 more source

HDsEMG data analysis using R-INLA

, 2018
The data contains the processed EMG data, the INLA results, and the codes used to run the INLA analysis and the figures. The data is to support the submission of an article titled "A novel approach towards understanding pain-induced spatio-temporal ...
Liew, B (via Mendeley Data), Liew, Bernard   +1 more
core   +1 more source

A Censored Seasonal Trend Model for marine environmental time series with R-INLA [PDF]

In the context of global change, the analysis of marine environmental time series is crucial for detecting trends, seasonality, and critical threshold exceedances, thereby supporting decision-making. However, these series often present quantification limits (i.e.
Soudant, Dominique, Courbariaux, Marie, Reynaud, Yann   +2 more
openaire   +1 more source

Generalized Additive Models (GAMs) via Bayesian P-splines using INLA

, 2019
IFCS 2015 celebrated Generalized Additive Models (GAMs) with its back-fitting algorithm. With P-splines a direct fitting algorithm is feasible. With a prior on the smoothing parameter, Bayesian GAMs are obtained, which allow credible intervals that ...
Cajo ter Braak (4511542)
core   +1 more source

A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$ [PDF]

, 2018
Spatial and spatio-temporal disease mapping models are widely used for the analysis of registry data and usually formulated in a hierarchical Bayesian framework. Explanatory variables can be included by a so-called ecological regression.
Held, Leonhard, Schrödle, Birgit
core