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Outliers and influential observations in the structural errors-in-variables model

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  • Myung Geun Kim

Abstract

The influence of observations on the parameter estimates for the simple structural errors-in-variables model with no equation error is investigated using the local influence method. Residuals themselves are not sufficient for detecting outliers. The likelihood displacement approach is useful for outlier detection especially when a masking phenomenon is present. An illustrative example is provided.

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  • Myung Geun Kim, 2000. "Outliers and influential observations in the structural errors-in-variables model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(4), pages 451-460.
    • Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:451-460
    DOI: 10.1080/02664760050003632
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    Cited by:

    1. Manuel Galea & Heleno Bolfarine & Filidor Vilcalabra, 2002. "Influence diagnostics for the structural errors-in-variables model under the Student-t distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(8), pages 1191-1204.
    2. Vidal, Ignacio & Arellano-Valle, Reinaldo B., 2010. "Bayesian inference for dependent elliptical measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2587-2597, November.
    3. Filidor Labra & Reiko Aoki & Heleno Bolfarine, 2005. "Local influence in null intercept measurement error regression under a student_t model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 723-740.
    4. 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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