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Comparison between a measurement error model and a linear model without measurement error

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  • Vidal, Ignacio
  • Iglesias, Pilar

Abstract

The regression of a response variable on an explanatory variable from observations on , where is a measurement of , is a special case of errors-in-variables model or measurement error model (MEM). In this work we attempt to answer the following question: given the data under a MEM, is it possible to not consider the measurement error on the covariable in order to use a simpler model? To the best of our knowledge, this problem has not been treated in the Bayesian literature. To answer that question, we compute Bayes factors, the deviance information criterion and the posterior mean of the logarithmic discrepancy. We apply these Bayesian model comparison criteria to two real data sets obtaining interesting results. We conclude that, in order to simplify the MEM, model comparison criteria can be useful to compare structural MEM and a random effect model, but we would also need other statistic tools and take into account the final goal of the model.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:92-102
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    References listed on IDEAS

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    1. Arellano-Valle, R.B. & Ozan, S. & Bolfarine, H. & Lachos, V.H., 2005. "Skew normal measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 265-281, October.
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    Cited by:

    1. Mário Castro & Ignacio Vidal, 2019. "Bayesian inference in measurement error models from objective priors for the bivariate normal distribution," Statistical Papers, Springer, vol. 60(4), pages 1059-1078, August.
    2. Jurecková, Jana & Picek, Jan & Saleh, A.K.Md. Ehsanes, 2010. "Rank tests and regression rank score tests in measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3108-3120, December.

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