Non-parametric quantile estimate for length-biased and right-censored data with competing risks

Feipeng Zhang; Yong Zhou

doi:10.1080/03610926.2016.1200092

Non-parametric quantile estimate for length-biased and right-censored data with competing risks

Feipeng Zhang
, Yong Zhou^*

^*Corresponding author for this work

Hunan Normal University
Shanghai University of Finance and Economics
CAS - Academy of Mathematics and System Sciences

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

4 Scopus citations

Abstract

In this article, we propose a non-parametric quantile inference procedure for cause-specific failure probabilities to estimate the lifetime distribution of length-biased and right-censored data with competing risks. We also derive the asymptotic properties of the proposed estimators of the quantile function. Furthermore, the results are used to construct confidence intervals and bands for the quantile function. Simulation studies are conducted to illustrate the method and theory, and an application to an unemployment data is presented.

Original language	English
Pages (from-to)	2407-2424
Number of pages	18
Journal	Communications in Statistics - Theory and Methods
Volume	47
Issue number	10
DOIs	https://doi.org/10.1080/03610926.2016.1200092
State	Published - 19 May 2018
Externally published	Yes

UN SDGs

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

SDG 8 Decent Work and Economic Growth

Keywords

Competing risks
Cumulative incidence function
Length-biased and right-censored data
Quantile

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10.1080/03610926.2016.1200092

Cite this

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title = "Non-parametric quantile estimate for length-biased and right-censored data with competing risks",

abstract = "In this article, we propose a non-parametric quantile inference procedure for cause-specific failure probabilities to estimate the lifetime distribution of length-biased and right-censored data with competing risks. We also derive the asymptotic properties of the proposed estimators of the quantile function. Furthermore, the results are used to construct confidence intervals and bands for the quantile function. Simulation studies are conducted to illustrate the method and theory, and an application to an unemployment data is presented.",

keywords = "Competing risks, Cumulative incidence function, Length-biased and right-censored data, Quantile",

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AU - Zhang, Feipeng

AU - Zhou, Yong

PY - 2018/5/19

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N2 - In this article, we propose a non-parametric quantile inference procedure for cause-specific failure probabilities to estimate the lifetime distribution of length-biased and right-censored data with competing risks. We also derive the asymptotic properties of the proposed estimators of the quantile function. Furthermore, the results are used to construct confidence intervals and bands for the quantile function. Simulation studies are conducted to illustrate the method and theory, and an application to an unemployment data is presented.

AB - In this article, we propose a non-parametric quantile inference procedure for cause-specific failure probabilities to estimate the lifetime distribution of length-biased and right-censored data with competing risks. We also derive the asymptotic properties of the proposed estimators of the quantile function. Furthermore, the results are used to construct confidence intervals and bands for the quantile function. Simulation studies are conducted to illustrate the method and theory, and an application to an unemployment data is presented.

KW - Competing risks

KW - Cumulative incidence function

KW - Length-biased and right-censored data

KW - Quantile

UR - https://www.scopus.com/pages/publications/85041492788

U2 - 10.1080/03610926.2016.1200092

DO - 10.1080/03610926.2016.1200092

M3 - 文章

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JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 10

ER -

Non-parametric quantile estimate for length-biased and right-censored data with competing risks

Abstract

UN SDGs

Keywords

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