Multivariate Hazard Rates under Random Censorship
The data consists of multivariate failure times under right random censorship. By the kernel smoothing technique, convolutions of cumulative multivariate hazard functions suggest estimators of the so-called multivariate hazard functions. We establish strong i.i.d. representations and uniform bounds of the remainder terms on some compact sets of the underlying space. Thus asymptotic normality and uniform consistency on such sets are obtained. The asymptotic mean squared error gives an optimal bandwidth by the plug-in method. Simulations assess the performance of our estimators.
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Volume (Year): 62 (1997)
Issue (Month): 2 (August)
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References listed on IDEAS
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- Dabrowska, Dorota M., 1989. "Kaplan-Meier estimate on the plane: Weak convergence, LIL, and the bootstrap," Journal of Multivariate Analysis, Elsevier, vol. 29(2), pages 308-325, May.
- Ruymgaart, F. H., 1989. "Some properties of bivariate empirical hazard processes under random censoring," Journal of Multivariate Analysis, Elsevier, vol. 28(2), pages 271-281, February.
- Xiang, X. J., 1994. "Law of the Logarithm for Density and Hazard Rate Estimation for Censored Data," Journal of Multivariate Analysis, Elsevier, vol. 49(2), pages 278-286, May.
- Gijbels, I. & Wang, J. L., 1993. "Strong Representations of the Survival Function Estimator for Truncated and Censored Data with Applications," Journal of Multivariate Analysis, Elsevier, vol. 47(2), pages 210-229, November.
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