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New Perspectives on Linear Calibration

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  • Kubokawa, T.
  • Robert, C. P.

Abstract

In univariate calibration, two standard estimators are usually opposed: the classical estimator and the inverse regression estimator. Controversies have followed the use of both estimators and we consider them from a decision-theoretic perspective, establishing the inadmissibility of the classical estimator and the admissibility of the inverse regression estimator. The latter allowing for a Bayesian interpretation, we also develop a fully noninformative study of the calibration model and derive a reference prior which avoids the inconsistency drawbacks of the inverse regression estimator.

Suggested Citation

  • Kubokawa, T. & Robert, C. P., 1994. "New Perspectives on Linear Calibration," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 178-200, October.
  • Handle: RePEc:eee:jmvana:v:51:y:1994:i:1:p:178-200
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    Cited by:

    1. Brunero Liseo, 2003. "Bayesian and conditional frequentist analyses of the Fieller’s problem. A critical review," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 133-150.
    2. Daniel Eno & Keying Ye, 2000. "Bayesian reference prior analysis for polynomial calibration models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 9(1), pages 191-208, June.
    3. Liao, Jason J. Z., 2002. "An insight into linear calibration: univariate case," Statistics & Probability Letters, Elsevier, vol. 56(3), pages 271-281, February.
    4. Vilca-Labra, F. & Arellano-Valle, R. B. & Bolfarine, H., 1998. "Elliptical Functional Models," Journal of Multivariate Analysis, Elsevier, vol. 65(1), pages 36-57, April.
    5. Yin, Ming, 2000. "Noninformative Priors for Multivariate Linear Calibration," Journal of Multivariate Analysis, Elsevier, vol. 73(2), pages 221-240, May.

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