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An insight into linear calibration: univariate case

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  • Liao, Jason J. Z.

Abstract

In the linear controlled calibration literature, the classical least-squares estimator and the inverse estimator are the two main estimators. These two have different advantages and disadvantages. Investigation of these differences leads us to propose a class of weighted least-squares estimators that includes the classical, the inverse, and the orthogonal-regression approaches as special cases. Instead of pre-choosing the weight, a method is proposed to choose the optimal weight. An example is used to demonstrate the advantages of the new approach.

Suggested Citation

  • Liao, Jason J. Z., 2002. "An insight into linear calibration: univariate case," Statistics & Probability Letters, Elsevier, vol. 56(3), pages 271-281, February.
    • Handle: RePEc:eee:stapro:v:56:y:2002:i:3:p:271-281
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    References listed on IDEAS

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    1. Kubokawa, T. & Robert, C. P., 1994. "New Perspectives on Linear Calibration," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 178-200, October.
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