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Estimation of the mean function with panel count data using monotone polynomial splines

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  • Minggen Lu
  • Ying Zhang
  • Jian Huang

Abstract

We study nonparametric likelihood-based estimators of the mean function of counting processes with panel count data using monotone polynomial splines. The generalized Rosen algorithm, proposed by Zhang & Jamshidian (2004), is used to compute the estimators. We show that the proposed spline likelihood-based estimators are consistent and that their rate of convergence can be faster than n-super-1/3. Simulation studies with moderate samples show that the estimators have smaller variances and mean squared errors than their alternatives proposed by Wellner & Zhang (2000). A real example from a bladder tumour clinical trial is used to illustrate this method. Copyright 2007, Oxford University Press.

Suggested Citation

  • Minggen Lu & Ying Zhang & Jian Huang, 2007. "Estimation of the mean function with panel count data using monotone polynomial splines," Biometrika, Biometrika Trust, vol. 94(3), pages 705-718.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:3:p:705-718
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    File URL: http://hdl.handle.net/10.1093/biomet/asm057
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