Influential Observations in the Functional Measurement Error Model

Ignacio Vidal, Pilar Iglesias and Manuel Galea

Journal of Applied Statistics, 2007, vol. 34, issue 10, pages 1165-1183

Abstract: In this work we propose Bayesian measures to quantify the influence of observations on the structural parameters of the simple measurement error model (MEM). Different influence measures, like those based on q-divergence between posterior distributions and Bayes risk, are studied to evaluate the influence. A strategy based on the perturbation function and MCMC samples is used to compute these measures. The samples from the posterior distributions are obtained by using the Metropolis-Hastings algorithm and assuming specific proper prior distributions. The results are illustrated with an application to a real example modeled with MEM in the literature.

Keywords: MEM; Influence measures; Bayes risk; q-divergence; Perturbation function; Metropolis-Hastings; Gibbs sampling (search for similar items in EconPapers)

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