Title: Bayesian local influence of transformation latent variable models with multivariate censored data
Authors: Ming Ouyang - The Chinese University of HongKong (China) [presenting]
Abstract: A Bayesian local influence method is developed for transformation latent variable models with multivariate censored data. The effects of minor perturbations to individual observations, the prior distributions of parameters, and the sampling distribution on the statistical inference are assessed through various perturbation schemes. The first-order influence measure is adopted to quantify the degree of minor perturbations to different aspects of a statistical model with the use of Bayes factor as an objective function. Simulation studies show that the empirical performance of the Bayesian local influence procedure is satisfactory.