Title
Simultaneous estimation for non-crossing multiple quantile regression with right censored data
Abstract
In this paper, we consider the estimation problem of multiple conditional quantile functions with right censored survival data. To account for censoring in estimating a quantile function, weighted quantile regression (WQR) has been developed by using inverse-censoring-probability weights. However, the estimated quantile functions from the WQR often cross each other and consequently violate the basic properties of quantiles. To avoid quantile crossing, we propose non-crossing weighted multiple quantile regression (NWQR), which estimates multiple conditional quantile functions simultaneously. We further propose the adaptive sup-norm regularized NWQR (ANWQR) to perform simultaneous estimation and variable selection. The large sample properties of the NWQR and ANWQR estimators are established under certain regularity conditions. The proposed methods are evaluated through simulation studies and analysis of a real data set.
Year
DOI
Venue
2016
10.1007/s11222-014-9482-0
Statistics and Computing
Keywords
Field
DocType
Multiple quantile regression,Non-crossing,Regularization,Sup-norm,Variable selection
Econometrics,Uniform norm,Feature selection,Quantile function,Quantile,Regularization (mathematics),Statistics,Censoring (statistics),Mathematics,Quantile regression,Estimator
Journal
Volume
Issue
ISSN
26
1
0960-3174
Citations 
PageRank 
References 
0
0.34
5
Authors
3
Name
Order
Citations
PageRank
Sungwan Bang1142.89
Hyungjun Cho21048.44
Myoungshic Jhun3276.75