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

Minggen Lu, Ying Zhang, Jian Huang

Research output: Contribution to journalArticle

56 Citations (Scopus)

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 n1/3. 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.

Original languageEnglish (US)
Pages (from-to)705-718
Number of pages14
JournalBiometrika
Volume94
Issue number3
DOIs
StatePublished - Sep 14 2007
Externally publishedYes

Fingerprint

Polynomial Splines
Count Data
Panel Data
Urinary Bladder Neoplasms
Splines
clinical trials
Monotone
Polynomials
Clinical Trials
Estimator
Tumors
Nonparametric Likelihood
sampling
Counting Process
methodology
Mean Squared Error
Spline
Tumor
Likelihood
Rate of Convergence

Keywords

  • Counting process
  • Empirical process
  • Isotonic regression
  • Maximum likelihood estimator
  • Maximum pseudolikelihood estimator
  • Monotone polynomial spline
  • Monte Carlo

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

Estimation of the mean function with panel count data using monotone polynomial splines. / Lu, Minggen; Zhang, Ying; Huang, Jian.

In: Biometrika, Vol. 94, No. 3, 14.09.2007, p. 705-718.

Research output: Contribution to journalArticle

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