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

^*此作品的通讯作者

统计学院

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

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

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.

源语言	英语
页（从-至）	902-916
页数	15
期刊	Acta Mathematicae Applicatae Sinica
卷	36
期	4
DOI	https://doi.org/10.1007/s10255-020-0972-x
出版状态	已出版 - 10月 2020

联合国可持续发展目标

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可持续发展目标 8 体面工作和经济增长

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10.1007/s10255-020-0972-x

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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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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

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

摘要

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