Sensitivity Analysis for Bayesian Hierarchical Models Page 1 Bayesian Analysis (2015) 10,
Number 2, pp. 321–349 Sensitivity Analysis for Bayesian Hierarchical Models Ma lgorzata …

Understanding model's sensitivity to its training data is crucial but can also be challenging
and costly, especially during training. To simplify such issues, we present the Memory …

Let D and V denote respectively Information Divergence and Total Variation Distance.
Pinsker's and Vajda's inequalities are respectively D≥[1/2] V 2 and D≥ log [(2+ V)/(2-V)]-[(2 …

Continuous (clustered) proportion data often arise in various domains of medicine and
public health where the response variable of interest is a proportion (or percentage) …

A new class of prior distributions that can be used to assess the sensitivity of the Bayesian
posterior inference to uncertainty in the data is proposed. This class is derived starting from …

We propose Bayesian case influence diagnostics for complex survival models. We develop
case deletion influence diagnostics for both the joint and marginal posterior distributions …

This paper introduces a robust Bayesian particle filter that can handle epistemic uncertainty
in the measurements, dynamics, and initial conditions. The robust filter returns robust …

Multi-group Bayesian structural equation modeling (MG-BSEM) gained considerable
attention among substantive researchers investigating cross-group differences and …

I consider a standard specification of the Bayesian linear model and derive necessary and
sufficient conditions for the variance of the case-deletion importance sampling weights to be …

Nonlinear mixed-effects (NLME) models are popular in many longitudinal studies, including
those on human immunodeficiency virus (HIV) viral dynamics, pharmacokinetic analysis …