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

Minggen Lu; Ying Zhang; Jian Huang

doi:10.1093/biomet/asm057

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

Journal article

Peer reviewed

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

Minggen Lu, Ying Zhang and Jian Huang

Biometrika, Vol.94(3), pp.705-718

2007

DOI: 10.1093/biomet/asm057

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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 . 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.

Details

Title: Subtitle: Estimation of the mean function with panel count data using monotone polynomial splines
Creators: Minggen Lu
Ying Zhang
Jian Huang
Resource Type: Journal article
Publication Details: Biometrika, Vol.94(3), pp.705-718
DOI: 10.1093/biomet/asm057
ISSN: 0006-3444
eISSN: 1464-3510
Language: English
Date published: 2007
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9983985928902771

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