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Efficient resampling methods for nonsmooth estimating functions.

Donglin Zeng1, D Y Lin

  • 1Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, NC 27599-7420, USA.

Biostatistics (Oxford, England)
|October 11, 2007
PubMed
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We developed a fast resampling method for estimating variances in biostatistics. This approach works for various statistical models, including quantile and rank regression, offering a significant speed improvement over existing techniques.

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Estimating variances for parameter estimators from nonsmooth estimating functions is crucial in biostatistics.
  • Existing resampling procedures can be computationally intensive, requiring the solution of estimating equations.

Purpose of the Study:

  • To propose a simple, general, and computationally efficient resampling strategy for variance estimation.
  • To apply the proposed method to semiparametric and nonparametric problems in biostatistics.

Main Methods:

  • A novel resampling strategy is introduced that avoids solving estimating equations.
  • The method's applicability is demonstrated using heteroscedastic quantile regression and censored data rank regression.

Main Results:

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  • The proposed strategy provides accurate variance estimates for parameter estimators.
  • It significantly outperforms existing methods in terms of computational speed.
  • Numerical results from simulated and real data confirm the method's effectiveness.

    • Conclusions:

    • The developed resampling strategy is a valuable tool for biostatistical analysis.
    • It offers a faster and more general alternative for variance estimation in various statistical models.