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Variance stabilization for a scalar parameter

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  • Thomas J. DiCiccio
  • Anna Clara Monti
  • G. Alastair Young

Abstract

Summary. We present a variance stabilizing transformation for inference about a scalar parameter that is estimated by a function of a multivariate M‐estimator. The transformation proposed is automatic, computationally simple and can be applied quite generally. Though it is based on an intuitive notion and entirely empirical, the transformation is shown to have an appropriate justification in providing variance stabilization when viewed from both parametric and nonparametric perspectives. Further, the transformation repairs deficiencies of existing methods for variance stabilization. The transformation proposed is illustrated in a range of examples, and its effectiveness to yield confidence limits having low coverage error is demonstrated in a numerical example.

Suggested Citation

  • Thomas J. DiCiccio & Anna Clara Monti & G. Alastair Young, 2006. "Variance stabilization for a scalar parameter," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 281-303, April.
    • Handle: RePEc:bla:jorssb:v:68:y:2006:i:2:p:281-303
    DOI: 10.1111/j.1467-9868.2006.00544.x
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    Cited by:

    1. Glenn Heller, 2021. "The added value of new covariates to the brier score in cox survival models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 1-14, January.
    2. Luca Greco & Laura Ventura, 2011. "Robust inference for the stress–strength reliability," Statistical Papers, Springer, vol. 52(4), pages 773-788, November.

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