Bayesian inference for additive mixed quantile regression models Quantile regression problems in practice may require flexible semiparametric forms of the predictor for modeling the dependence of responses on covariates... RY Yu,H Rue - 《Computational Statistics & Data Analysis》 被引量: 230发表: 2011...
Bayesian regression modeling with INLA. XiaofengWang, Yu R.Yue and Julian J.Faraway. Boca Raton: CRC Press.doi:info:doi/10.1111/biom.13128Tabb, Loni P.Biometrics
In solid black line, regression line 1:1. Figure 4. Comparison of estimated values of disease incidence, odds ratios and incidence ratios obtained with the four fitted models. (a) Disease incidence for DSS (blue), calendar (green) and untreated (pink) treatments; (b) odds ratio for ...
& 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...
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 ...
Quantile regression problems in practice may require flexible semiparametric forms of the predictor for modeling the dependence of responses on covariates. Furthermore, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudin...
(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 ...
INLA SPDE is a modeling framework implemented in the statistical software R (R Development Core Team, 2008) that allows many types of models, including spatiotemporal analyses, to be treated as hierarchical Bayesian models with spatial, temporal, and regression components. A key advantage of INLA...
Model fit and prediction was done using the SPDE method and INLA algorithm for the fast approximation of the marginal posterior distributions. In the SPDE/INLA approach, the spatial process is represented as a Gaussian Markov random field (GMRF) with mean zero and a symmetric positive definite ...
The works10,11 proposed a continuous inhomogeneous DBN, which assumes a fixed network structure and only allows the parameters to vary with time. The works12,13 proposed an alterna- tive continuous regression-based time-varying DBN with node-specific change points, that is, network structures ...