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Efficiency Considerations in the Additive Hazards Model with Current Status Data

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  • D. Ghosh

Abstract

For current status data, LIN, OAKES and Ying (1998) proposed a procedure for estimation of the regression parameters in the additive hazards model that makes clever use of martingale theory. However, one of the outstanding problems posed in the paper was the issue of efficient estimation, as their estimators do not attain the semiparametric information bound. In this paper, we explore this issue and provide a characterization of the NPMLE. We conduct efficiency comparisons between the NPMLE and the procedure of LINet al. (1998) analytically and numerically through analysis of a dataset from a tumorigenicity experiment.

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  • D. Ghosh, 2001. "Efficiency Considerations in the Additive Hazards Model with Current Status Data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(3), pages 367-376, November.
    • Handle: RePEc:bla:stanee:v:55:y:2001:i:3:p:367-376
    DOI: 10.1111/1467-9574.00175
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    Cited by:

    1. Junlong Li & Chunjie Wang & Jianguo Sun, 2012. "Regression analysis of clustered interval-censored failure time data with the additive hazards model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 1041-1050, December.
    2. Li, Jinqing & Ma, Jun, 2019. "Maximum penalized likelihood estimation of additive hazards models with partly interval censoring," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 170-180.
    3. Debashis Ghosh, 2003. "Goodness-of-Fit Methods for Additive-Risk Models in Tumorigenicity Experiments," Biometrics, The International Biometric Society, vol. 59(3), pages 721-726, September.
    4. Xiaoguang Wang & Ziwen Wang, 2021. "EM algorithm for the additive risk mixture cure model with interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 91-130, January.
    5. Debashis Ghosh, 2004. "Nonparametric and semiparametric inference for models of tumor size and metastasis," The University of Michigan Department of Biostatistics Working Paper Series 1035, Berkeley Electronic Press.

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