Bootstrapping quantiles in a fixed design regression model with censored data |
| |
| Institution: | 1. Escola Nacional de Ciências Estatísticas, Brazil;2. Instituto de Matemática, Universidade Federal do Rio de Janeiro, Brazil;3. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Canada;1. Key Laboratory of MADIS, Academy of Mathematics and Systems Science), Chinese Academy of Sciences, Beijing 100190, China;2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology), Chinese Academy of Sciences, Beijing 100190, China;3. University of Chinese Academy of Sciences, Beijing 100049, China;1. National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics (SMAS), University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia;2. School of Mathematics, University of Bristol, Tyndall Avenue, Bristol, BS8 1TH, UK;1. Centre of Excellence PLECO, Department of Biology, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk, Belgium;2. Department of Silviculture and Forest Production, National Forest Centre – Forest Research Institute Zvolen, T.G. Masaryka 22, 960 92 Zvolen, Slovak Republic;3. Department of Forest Management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamýcká 129, 165 21 Praha – Suchdol, Czech Republic;4. Department of Meteorology, Eötvös Loránd University, Pázmány P. sétány 1/A, H-1117 Budapest, Hungary;5. Institute of Forest Ecology, Slovak Academy of Sciences, Ľ. Štúra 2, 96053 Zvolen, Slovakia;6. Department of Forest Management and Geodesy, Faculty of Forestry, Technical University in Zvolen, T.G. Masaryka 24, 96053 Zvolen, Slovakia;1. National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics (SMAS), University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia;2. School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK |
| |
| Abstract: | We consider the problem of estimating the quantiles of a distribution function in a fixed design regression model in which the observations are subject to random right censoring. The quantile estimator is defined via a conditional Kaplan-Meier type estimator for the distribution at a given design point. We establish an a.s. asymptotic representation for this quantile estimator, from which we obtain its asymptotic normality. Because a complicated estimation procedure is necessary for estimating the asymptotic bias and variance, we use a resampling procedure, which provides us, via an asymptotic representation for the bootstrapped estimator, with an alternative for the normal approximation. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
