Nonparametric k-sample tests with panel count data

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

We study the nonparametric k-sample test problem with panel count data. The asymptotic normality of a smooth functional of the nonparametric maximum pseudo-likelihood estimator (Wellner & Zhang, 2000) is established under some mild conditions. We construct a class of easy-to-implement nonparametric tests for comparing mean functions of k populations based on this asymptotic normality. We conduct various simulations to validate and compare the tests. The simulations show that the tests perform quite well and generally have good power to detect differences among the mean functions. The method is illustrated with a real-life example.

Original languageEnglish (US)
Pages (from-to)777-790
Number of pages14
JournalBiometrika
Volume93
Issue number4
DOIs
StatePublished - Dec 1 2006
Externally publishedYes

Fingerprint

Count Data
Panel Data
Asymptotic Normality
Pseudo-maximum Likelihood
Non-parametric test
Population
Maximum likelihood
Test Problems
Simulation
testing
Estimator
sampling
Count data
Asymptotic normality
Class
Life
methodology

Keywords

  • Counting process
  • Empirical process
  • Interval censored data
  • Isotonic regression
  • 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

Nonparametric k-sample tests with panel count data. / Zhang, Ying.

In: Biometrika, Vol. 93, No. 4, 01.12.2006, p. 777-790.

Research output: Contribution to journalArticle

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