Nonparametric Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling

Fei Peng Zhang; Cai Yun Fan; Yong Zhou

doi:10.1007/s10255-020-0972-x

Nonparametric Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling

Fei Peng Zhang^*
, Cai Yun Fan
, Yong Zhou

^*Corresponding author for this work

School of Statistics

Xi'an Jiaotong University
Shanghai University of International Business and Economics

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	902-916
Number of pages	15
Journal	Acta Mathematicae Applicatae Sinica
Volume	36
Issue number	4
DOIs	https://doi.org/10.1007/s10255-020-0972-x
State	Published - Oct 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

Keywords

62G05
62N01
Length-biased data
competing risks
estimating equation
quantile residual life

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10.1007/s10255-020-0972-x

Cite this

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title = "Nonparametric Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling",

abstract = "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.",

keywords = "62G05, 62N01, Length-biased data, competing risks, estimating equation, quantile residual life",

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AU - Zhou, Yong

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Y1 - 2020/10

N2 - 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.

AB - 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.

KW - 62G05

KW - 62N01

KW - Length-biased data

KW - competing risks

KW - estimating equation

KW - quantile residual life

UR - https://www.scopus.com/pages/publications/85098200702

U2 - 10.1007/s10255-020-0972-x

DO - 10.1007/s10255-020-0972-x

M3 - 文章

AN - SCOPUS:85098200702

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JO - Acta Mathematicae Applicatae Sinica

JF - Acta Mathematicae Applicatae Sinica

IS - 4

ER -

Nonparametric Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling

Abstract

UN SDGs

Keywords

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