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Influential Observations in the Functional Measurement Error Model

Author

Listed:
  • Ignacio Vidal
  • Pilar Iglesias
  • Manuel Galea

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.

Suggested Citation

  • Ignacio Vidal & Pilar Iglesias & Manuel Galea, 2007. "Influential Observations in the Functional Measurement Error Model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(10), pages 1165-1183.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1165-1183
    DOI: 10.1080/02664760701592703
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    Cited by:

    1. Casanova, María P. & Iglesias, Pilar & Bolfarine, Heleno & Salinas, Victor H. & Peña, Alexis, 2010. "Semiparametric Bayesian measurement error modeling," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 512-524, March.
    2. Vidal, Ignacio & Iglesias, Pilar, 2008. "Comparison between a measurement error model and a linear model without measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 92-102, September.

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