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

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 ...

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 ...

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 ...

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 ...

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

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 ...