Nonparametric Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling
| Autor: | Caiyun Fan, Fei-Peng Zhang, Yong Zhou |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Length biased sampling
Applied Mathematics Nonparametric statistics Estimator Inference Sampling (statistics) Quantile function Residual 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Econometrics 030212 general & internal medicine 0101 mathematics Mathematics Quantile |
| Zdroj: | Acta Mathematicae Applicatae Sinica, English Series. 36:902-916 |
| ISSN: | 1618-3932 0168-9673 |
| Popis: | This paper considers a competing risks model for right-censored and length-biased survival data from prevalent sampling. We propose a nonparametric quantile inference procedure for cause-specific residual life distribution with competing risks data. We also derive the asymptotic properties of the proposed estimators of this quantile function. Simulation studies and the unemployment data demonstrate the practical utility of the methodology. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink