Foundational work comparing Bayesian estimation with classical methods in multilevel SEM further emphasises the promise of the Bayesian framework, particularly under small-sample conditions ...

In another study, advanced multilevel two-part modelling techniques have been applied to ecological momentary assessment data to disentangle the dual processes affecting dietary intake ...

A time series multilevel model is developed to produce monthly unemployment figures at a provincial level and quarterly figures at a municipal level. The model is formulated in an hierarchical ...

We implement a hierarchical Bayesian approach to parameterize widely used depth‐damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts ...

Statistical decision theory: risk, decision rules, loss and utility functions, Bayesian expected loss, ... Hierarchical/ Multilevel Models, Cluster Analysis and Mixture Modeling. Teaching. This course ...

Our template 2LevelMissingOnePass is a fully Bayesian procedure that requires the specification of the model of interest and the model for imputing missing values, and produces a standard MCMC chain ...

This study compares two different techniques in a time series small area application: state space models estimated with the Kalman filter with a frequentist approach to hyperparameter estimation, and ...

Modeling space-time variability using multivariate semi-Bayesian hierarchical framework for seasonal total and extreme precipitation in the U.S. Northern Great Plains By Prasad Thota We adapt a ...

A novel Bayesian Hierarchical Network Model (BHNM) is designed for ensemble predictions of daily river stage, leveraging the spatial interdependence of river networks and hydrometeorological variables ...

Similar to generalised linear models, multilevel models accommodate any distribution that is a member of the ... The most recent development in estimation procedures focus on Bayesian methods.