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Linear Models and Spurious Observations

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  • Bovas Abraham
  • George E. P. Box

Abstract

A Bayesian approach is adopted here to make inferences about the parameters of a linear model in the possible presence of one or more spurious observations. The method proposed is illustrated by analysing a classical set of data.

Suggested Citation

  • Bovas Abraham & George E. P. Box, 1978. "Linear Models and Spurious Observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(2), pages 131-138, June.
    • Handle: RePEc:bla:jorssc:v:27:y:1978:i:2:p:131-138
    DOI: 10.2307/2346940
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    Cited by:

    1. Hamura, Yasuyuki & Irie, Kaoru & Sugasawa, Shonosuke, 2022. "Log-regularly varying scale mixture of normals for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    2. Tiao, George C., 1991. "Bayesian outliers functions for linear models," UC3M Working papers. Economics 5816, Universidad Carlos III de Madrid. Departamento de Economía.
    3. B. Abraham & W. Wei, 1984. "Inferences about the parameters of a time series model with changing variance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 31(1), pages 183-194, December.
    4. Hans, Christopher M. & Peruggia, Mario & Wang, Junyan, 2023. "Empirical Bayes Model Averaging with Influential Observations: Tuning Zellner’s g Prior for Predictive Robustness," Econometrics and Statistics, Elsevier, vol. 27(C), pages 102-119.
    5. Guttman, Irwin, 1992. "A Bayesian look at diagnostics in the univariate linear model," UC3M Working papers. Economics 2831, Universidad Carlos III de Madrid. Departamento de Economía.
    6. Justel, Ana & Sánchez, María Jesús, 1994. "Grupos atípicos en modelos econométricos," DES - Documentos de Trabajo. Estadística y Econometría. DS 10755, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Justel, A., 1998. "Heterogeneity and model uncertainty in bayesian regression models," DES - Working Papers. Statistics and Econometrics. WS 6260, Universidad Carlos III de Madrid. Departamento de Estadística.

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