Bayesian regression modeling with INLA. Wang, Xiaofeng, Yue, Yu R. and Faraway, Julian J.. Boca Raton: CRC Press.doi:10.1111/biom.13128Loni P. Tabb
& Rue, H. Bayesian spatial modelling with R-INLA. J. Stat. Soft. 63, 1–25 (2015). Google Scholar Vanhatalo, J. et al. GPstuff: Bayesian modeling with Gaussian processes. J. Mach. Learn. Res. 14, 1175–1179 (2013). MathSciNet MATH Google Scholar Blaxter, L. How to Research...
Bayesian approaches play an important role in the development of new spatial econometric methods, but are uncommon in applied work. This is partly due to a
In any case, the type of modeling framework proposed could be adapted to other types of partitions, regular or irregular, more or less fine, of the study area under consideration. Dealing with the Temporal Uncertainty The logistic regression model represented by (1) implicitly assumes that the ...
Martins, T. G., D. Simpson, F. Lindgren, and H. Rue. 2013. “Bayesian Computing with INLA: New Features.”Computational Statistics & Data Analysis67:68–83. https://doi.org/10.1016/j.csda.2013.04.014. Web of Science ®Google Scholar ...
Spatial and spatio-temporal Bayesian models with R-INLA. Hoboken: John Wiley & Sons; 2015. Book Google Scholar West BT, Welch KB, Galecki AT. Linear mixed models: a practical guide using statistical software. Boca Raton: Chapman and Hall/CRC; 2006. Book Google Scholar Wangdi K, Canav...
structure and temporal correlation simultaneously. In both the generalized estimation equation and the hierarchical Poisson regression modeling frameworks, the autoregressive terms have been found to significantly outperform unstructured terms in model fit [7,15]....
We calculated the absolute numbers of new cases and deaths based on the changes in mortality rates. The BAPC model was implemented using the BAPC package in R and nested Laplace approximation based on INLA in R (version 4.4.1). Results Gender and age Gender In 2019, the estimated number ...
(ssi,t), the underlying unknown COVID-19 infection risk in MSOAiand weekt. The log-infection risk is modelled by two components, the first of which is the vector ofpknown covariatesxx(ssi)=(1,x1(ssi),…,xp(ssi))related to locationssi, including an intercept term, with regression ...
For Gaussian latent variables, the computational task can be efficiently solved through the integrated nested Laplace approximation (INLA) approach (Rue et al., 2009). Hubin and Storvik (2016) compare INLA with MCMC based methods, showing that INLA based approximations are extremely accurate and ...