Estimating Bivariate Survival Function by Volterra Estimator Using Dynamic Programming Techniques

For estimating bivariate survival function under random censorship, it is commonly believed that the Dabrowska estimator is among the best ones while the Volterra estimator is far from being computational efficiency. As we will see, the Volterra estimator is a natural extension of the Kaplan-Meier estimator to bivariate data setting. We believe that the computational 'inefficiency' of the Volterra estimator is largely due to the formidable computational complexity of the traditional recursion method. In this paper, we show by numerical study as well as theoretical analysis that the Volterra estimator, once computed by dynamic programming technique, is more computationally efficient than the Dabrowska estimator. Therefore, the Volterra estimator with dynamic programming would be quite recommendable in applications owing to its significant computational advantages.

並列關鍵字

Bivariate survival function ； computational complexity ； Dabrowska estimator ； dynamic programming ； Kaplan-Meier estimator ； Volterra estimator

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