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Hazard Rate Estimation for Censored Data via Strong Representation of the Kaplan-Meier Estimator.
CALIFORNIA UNIV DAVIS INTERCOLLEGE DIV OF STATISTICS
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This document studies the estimation of a hazard rate function based on censored data by the kernel smoothing method. Our technique is facilitated by a recent result of Lo and Singh 1984 which establishes a strong uniform approximation of the Kaplan-Meier estimator by an average of independent random variables. Pointwise and uniform strong consistency are derived, as well as the mean squared error expression and asymptotic normality, which is obtained using a more traditional method, as compared with the Hajek projection employed by Tanner and Wong 1983. Author
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