The aim of this paper is to develop a Bayesian local influence method (Zhu et al. 2009,
submitted) for assessing minor perturbations to the prior, the sampling distribution, and …

This article develops a variety of influence measures for carrying out perturbation (or
sensitivity) analysis to joint models of longitudinal and survival data (JMLS) in Bayesian …

In this paper we develop a general framework of Bayesian influence analysis for assessing
various perturbation schemes to the data, the prior and the sampling distribution for a class …

Diagnostic methods have been an important tool in regression analysis to detect anomalies,
such as departures from error assumptions and the presence of outliers and influential …

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

We use the local influence approach to develop influence measures for identifying
observations that strike a disproportionate effect on the maximum likelihood estimate of …

We examine three Bayesian case influence measures including the φ-divergence, Cook's
posterior mode distance, and Cook's posterior mean distance for identifying a set of …

In this paper we show how to evaluate the effect that perturbations to the model, data, or
case weights have on maximum likelihood estimates from censored survival data. The ideas …

Bayesian influence measures for linear regression models have been developed mostly for
normal regression models with noninformative prior distributions for the unknown …

Methods for summarizing case influence in Bayesian models take essentially two forms:(1)
use common divergence measures for calculating distances between the full-data posterior …