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Nonparametric k-sample tests with panel count data

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  • Ying Zhang

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. Copyright 2006, Oxford University Press.

Suggested Citation

  • Ying Zhang, 2006. "Nonparametric k-sample tests with panel count data," Biometrika, Biometrika Trust, vol. 93(4), pages 777-790, December.
  • Handle: RePEc:oup:biomet:v:93:y:2006:i:4:p:777-790
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    File URL: http://hdl.handle.net/10.1093/biomet/93.4.777
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    Cited by:

    1. Li, Yang & Zhao, Hui & Sun, Jianguo & Kim, KyungMann, 2014. "Nonparametric tests for panel count data with unequal observation processes," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 103-111.
    2. Xin He & Xuenan Feng & Xingwei Tong & Xingqiu Zhao, 2017. "Semiparametric partially linear varying coefficient models with panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 439-466, July.
    3. Xin He & Xuenan Feng & Xingwei Tong & Xingqiu Zhao, 0. "Semiparametric partially linear varying coefficient models with panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 0, pages 1-28.
    4. Xingqiu Zhao & Jianguo Sun, 2011. "Nonparametric Comparison for Panel Count Data with Unequal Observation Processes," Biometrics, The International Biometric Society, vol. 67(3), pages 770-779, September.
    5. Xingqiu Zhao & N. Balakrishnan & Jianguo Sun, 2011. "Nonparametric inference based on panel count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 1-42, May.
    6. Zhao, Xingqiu & Tong, Xingwei, 2011. "Semiparametric regression analysis of panel count data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 291-300, January.
    7. Zhao, Xingqiu & Tong, Xingwei & Sun, Jianguo, 2013. "Robust estimation for panel count data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 33-40.
    8. Haiying Wang & Yang Li & Jianguo Sun, 2015. "Focused and Model Average Estimation for Regression Analysis of Panel Count Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 732-745, September.
    9. Yao, Bin & Wang, Lianming & He, Xin, 2016. "Semiparametric regression analysis of panel count data allowing for within-subject correlation," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 47-59.
    10. Gang Cheng & Ying Zhang & Liqiang Lu, 2011. "Efficient algorithms for computing the non and semi-parametric maximum likelihood estimates with panel count data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 567-579.
    11. C. Dean & E. Juarez Colunga, 2011. "Comments on: Nonparametric inference based on panel count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 43-45, May.
    12. Balakrishnan, N. & Zhao, Xingqiu, 2010. "A nonparametric test for the equality of counting processes with panel count data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 135-142, January.

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