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Efficient algorithms for computing the non and semi-parametric maximum likelihood estimates with panel count data

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  • Gang Cheng
  • Ying Zhang
  • Liqiang Lu

Abstract

Nonparametric and semi-parametric analysis of panel count data have recently been active research topics in statistical literature. The maximum likelihood method based on the non-homogeneous Poisson process has been proved an efficient inference procedure for such analysis. However, computing the non- and semi-parametric maximum likelihood estimates (MLEs) can be very intensive numerically and the available methods are not efficient. In this manuscript, we develop an efficient numerical algorithm stemming from the Newton–Raphson method to compute the non- and semi-parametric MLEs for panel count data. Simulation studies are carried out to demonstrate the numerical efficiency of the proposed algorithm compared to the existing methods in the literature.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:2:p:567-579
    DOI: 10.1080/10485252.2010.548521
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    Cited by:

    1. Liang Zhu & Ying Zhang & Yimei Li & Jianguo Sun & Leslie L. Robison, 2018. "A semiparametric likelihood†based method for regression analysis of mixed panel†count data," Biometrics, The International Biometric Society, vol. 74(2), pages 488-497, June.
    2. Yimei Li & Liang Zhu & Lei Liu & Leslie L. Robison, 2021. "Regression Analysis of Mixed Panel-Count Data with Application to Cancer Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 178-195, April.
    3. Yudong Wang & Zhi‐Sheng Ye & Hongyuan Cao, 2021. "On computation of semiparametric maximum likelihood estimators with shape constraints," Biometrics, The International Biometric Society, vol. 77(1), pages 113-124, March.
    4. Audrey Boruvka & Richard J. Cook, 2015. "A Cox-Aalen Model for Interval-censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 414-426, June.
    5. Fei Qin & Zhangsheng Yu, 2021. "Penalized spline estimation for panel count data model with time-varying coefficients," Computational Statistics, Springer, vol. 36(4), pages 2413-2434, December.

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