Li, Gang; Hollander, Myles; McKeague, Ian W.; Yang, Jie Nonparametric likelihood ratio confidence bands for quantile functions from incomplete survival data. (English) Zbl 0859.62047 Ann. Stat. 24, No. 2, 628-640 (1996). Summary: 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. Cited in 20 Documents MSC: 62G15 Nonparametric tolerance and confidence regions 62G07 Density estimation 62G20 Asymptotic properties of nonparametric inference Keywords:empirical likelihood; Hall-Wellner band; Kaplan-Meier estimator; multiplicative intensity model; censoring; truncation; confidence bands; quantile functions; nonparametric likelihood ratio approach; Monte Carlo study PDFBibTeX XMLCite \textit{G. Li} et al., Ann. Stat. 24, No. 2, 628--640 (1996; Zbl 0859.62047) Full Text: DOI References: [1] AALEN, O. O. 1978. Nonparametric inference for a family of counting processes. Ann. Statist. 6 701 726. Z. · Zbl 0389.62025 [2] ALY, E.-E. A. A., CSORGO, M. and HORVATH, L. 1985. Strong approximation of the quantile \" ṕrocess of the product-limit estimator. J. Multivariate Anal. 16 185 210. Z. · Zbl 0577.62042 [3] ANDERSEN, P. K., BORGAN, O., GILL, R. D. and KEIDING, N. 1993. Statistical Models Based on Counting Processes. Springer, New York. Z. · Zbl 0769.62061 [4] DICICCIO, T. 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