Statistical analysis with missing data
Statistical analysis with missing data
Bayesian local influence for the growth curve model with Rao's simple covariance structure
Journal of Multivariate Analysis
Sensitivity analysis of structural equation models with equality functional constraints
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Influence diagnostics in log-Birnbaum-Saunders regression models with censored data
Computational Statistics & Data Analysis
Log-Burr XII regression models with censored data
Computational Statistics & Data Analysis
Deletion measures for generalized linear mixed effects models
Computational Statistics & Data Analysis
A robust extension of the bivariate Birnbaum-Saunders distribution and associated inference
Journal of Multivariate Analysis
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A method is proposed in this paper to assess the local influence of minor perturbations for a nonlinear structural equation model with missing data that are missing at random. The main idea is to apply Zhu and Lee's (J. Roy. Statist. Soc. Ser. B 63 (2001) 111) approach to the conditional expectation of the complete-data log-likelihood function in the corresponding EM algorithm for deriving the conformal normal curvature. Building blocks for achieving the diagnostic measures are computed via latent variables that are generated by the Gibbs sampler and Metropolis-Hastings algorithm. It is shown that the proposed methodology is feasible for a wide variety of perturbation schemes. To illustrate the methodology, results that are obtained from analyses of some artificial examples, a simulation study, and a real example are presented.