Nonparametric likelihood ratio confidence bands for quantile functions from incomplete survival data
Li, Gang ; Hollander, Myles ; McKeague, Ian W. ; Yang, Jie
Ann. Statist., Tome 24 (1996) no. 6, p. 628-640 / Harvested from Project Euclid
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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.
Publié le : 1996-04-14
Classification:  Empirical likelihood,  Hall-Wellner band,  Kaplan-Meier estimator,  multiplicative intensity model,  censoring,  truncation,  62G07,  62G20 
@article{1032894455,
     author = {Li, Gang and Hollander, Myles and McKeague, Ian W. and Yang, Jie},
     title = {Nonparametric likelihood ratio confidence bands for quantile functions from incomplete survival data},
     journal = {Ann. Statist.},
     volume = {24},
     number = {6},
     year = {1996},
     pages = { 628-640},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1032894455}
}

Li, Gang; Hollander, Myles; McKeague, Ian W.; Yang, Jie. Nonparametric likelihood ratio confidence bands for quantile functions from incomplete survival data. Ann. Statist., Tome 24 (1996) no. 6, pp.  628-640. http://gdmltest.u-ga.fr/item/1032894455/