Abstract
The purpose of this paper is to derive confidence bands for quantile functions using a nonparametric likelihood ratio approach. The method is easy to implement and has several appealing properties. It applies to right-censored and left-truncated data, and it does not involve density estimation or even require the existence of a density. Previous approaches (e.g., bootstrap) have imposed smoothness conditions on the density. The performance of the proposed method is investigated in a Monte Carlo study, and an application to real data is given.
Citation
Gang Li. Myles Hollander. Ian W. McKeague. Jie Yang. "Nonparametric likelihood ratio confidence bands for quantile functions from incomplete survival data." Ann. Statist. 24 (2) 628 - 640, April 1996. https://doi.org/10.1214/aos/1032894455
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