In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies.
For this research, another Bayesian method, hierarchical Bayesian logistic regression (HB), is applied and compared with the HSM. For this method, a mixture of three normal distributions was used to estimate location effects and handle an asymmetrical long-tailed crash frequency distribution. The ...
We propose a Bayesian hierarchical linear log-contrast model for compositional data which is estimated by mean field Monte Carlo co-ordinate ascent variational inference. We use the alr transformation within a log-contrast model which removes the need to specify a reference category. Sparse variable ...
For the problem at hand, the hierarchical Bayesian neural network approach is shown to be superior to the approach based on hierarchical Bayesian logistic regression model as well as the classical feedforward neural networks.doi:10.1198/016214504000000665...
Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations 2015, Accident Analysis and Prevention Citation Excerpt : Correlated random parameters models are able to model sophisticated interactions effects, but the cross-level interactio...
Gilardi A, Mateu J, Borgoni R, Lovelace R (2022) Multivariate hierarchical analysis of car crashes data considering a spatial network lattice. J R Stat Soc Ser A 185(3):1150–1177 Article Google Scholar Grolemund G, Wickham H (2011) Dates and times made easy with lubridate. J Stat Soft...
“intraclass dependency” among the observations within units at the higher level of the hierarchy. The hierarchical l logistic regression analysis considers the variations due to hierarchy structure in the data. It allows the simultaneous examination of the effects of group level and individual level ...
we will also consider a model with only the first two levels in the BLASSO-NEG. This two level hierarchical model only has one hyperparameterλto be adjusted, and thus, it requires less computation. We name the logistic regression model with the two-level prior as BLASSO-NE. We will nex...
Reference Carlin, B. P., A. E. Gelfand, and A. F. M. Smith. 1992. Hierarchical Bayesian analysis of changepoint problems.Journal of the Royal Statistical Society, Series C41: 389–405.
Linear mixed models also known as ‘multilevel or hierarchical models’, are a type of regression model which takes into account both fixed and random effects. They are particularly used when there is non-independence in the data (as in a case where we have patient level data along with kne...