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 with INLA. Contribute to julianfaraway/brinla development by creating an account on GitHub.
Review of Bayesian Regression Modelling with INLA by Xiaofeng Wang, Yu Ryan Yue, and Julian J. FarawayNo abstract is available for this item.doi:10.1007/s13253-018-00339-xMorrison, KathrynSpringer-VerlagJournal of Agricultural Biological & Environmental Statistics...
Dealing with the Temporal Uncertainty The logistic regression model represented by (1) implicitly assumes that the day of the week (DoW) and the week within the year are known exactly for each event/control location, i. Then, DoW(i) and w(i) are two known values and the corresponding fix...
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...
2008. Spatial Regression Models. Thousand Oaks: Sage Publications, Inc. [Google Scholar] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://...
Tutorial: advanced Bayesian regression in JASP. Zenodo https://doi.org/10.5281/zenodo.3991325 (2020). Article Google Scholar O’Hagan, A. et al. Uncertain Judgements: Eliciting Experts’ Probabilities (Wiley, 2006). This book presents a great collection of information with respect to prior ...
(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 ...
Bayesian geostatistical regression (GR) models capture the spatial correlation present in the pollutant concentrations and provide estimates of the prediction uncertainty. Furthermore, they allow a straightforward assessment of the exposure burden through high-resolution population estimates, since the ...
The works12,13 proposed an alterna- tive continuous regression-based time-varying DBN with node-specific change points, that is, network structures associated with different nodes are allowed to change with time in different ways. These extended DBN models, however, still have obvious limitations, ...