Estimating Bivariate Survival Function by Volterra Estimator Using Dynamic Programming Techniques

Autor:	Pablo Zafra, Jiantian Wang
Rok vydání:	2021
Předmět:	Mathematical optimization Efficient estimator Minimum-variance unbiased estimator Bivariate data Stein's unbiased risk estimate Estimator Bivariate analysis Minimax estimator Invariant estimator Mathematics
Zdroj:	Journal of Data Science. 7:365-380
ISSN:	1683-8602 1680-743X
Popis:	For estimating bivariate survival function under random censor- ship, it is commonly believed that the Dabrowska estimator is among the best ones while the Volterra estimator is far from being computational ef- ficiency. 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 recom- mendable in applications owing to its significant computational advantages.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1ea604033d86a7df07deb1cd316675ac https://doi.org/10.6339/jds.2009.07(3).489 Zobrazit plný text záznamu