2010/07/20 by Stanislav Volgushev, Holger Dette, Volgushev, Stanislav +1 · 2 citations
Computer Science · Mathematics · #Asymptotic distribution #Bayesian Methods and Mixture Models #Computer science #Consistency (knowledge bases) #Convergence (economics) #Cumulative distribution function #Discrete mathematics #Econometrics #Economics #Estimator #FOS: Computer and information sciences #Kaplan–Meier estimator #Mathematics #Methodology (stat.ME) #Monotone polygon #Nonparametric regression #Nonparametric statistics #Probability density function #Quantile #Quantile function #Quantile regression #Sample (material) #Statistical Distribution Estimation and Applications #Statistical Methods and Inference #Statistics #Weak convergence #stat.ME
paper · pdf · doi:10.48550/arxiv.1007.3376
published in arXiv (Cornell University) (Cornell University) · 46 pages, 24 figures
openalex publication_date 2010/07/20 · arxiv created 2012/06/12 · arxiv updated 2012/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We consider the problem of nonparametric quantile regression for twice censored data. Two new estimates are presented, which are constructed by applying concepts of monotone rearrangements to estimates of the conditional distribution function. The proposed methods avoid the problem of crossing quantile curves. Weak uniform consistency and weak convergence is established for both estimates and their finite sample properties are investigated by means of a simulation study. As a by-product, we obtain a new result regarding the weak convergence of the Beran estimator for right censored data on the maximal possible domain, which is of its own interest.