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 STYLE
Li, G., Hollander, M., McKeague, I. W., & Yang, J. (1996). Nonparametric likelihood ratio confidence bands for quantile functions from incomplete survival data. Annals of Statistics, 24(2), 628–640. https://doi.org/10.1214/aos/1032894455
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