A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator

Zhiping Qiu; Xiaoping Chen; Yong Zhou

doi:10.1016/j.spl.2014.12.006

A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator

Zhiping Qiu
, Xiaoping Chen
, Yong Zhou^*

^*Corresponding author for this work

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

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

10 Scopus citations

Abstract

In this paper, a nonparametric imputation method is developed for the additive hazards model when the censoring indicator is missing at random (MAR). The asymptotic properties of the proposed estimator are derived and the performance of the proposed estimator is demonstrated by a numerical simulation.

Original language	English
Pages (from-to)	89-97
Number of pages	9
Journal	Statistics and Probability Letters
Volume	98
DOIs	https://doi.org/10.1016/j.spl.2014.12.006
State	Published - 1 Mar 2015
Externally published	Yes

Keywords

Additive hazards model
Imputation
Kernel smoothing
Missing at random
Missing data

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10.1016/j.spl.2014.12.006

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title = "A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator",

abstract = "In this paper, a nonparametric imputation method is developed for the additive hazards model when the censoring indicator is missing at random (MAR). The asymptotic properties of the proposed estimator are derived and the performance of the proposed estimator is demonstrated by a numerical simulation.",

keywords = "Additive hazards model, Imputation, Kernel smoothing, Missing at random, Missing data",

author = "Zhiping Qiu and Xiaoping Chen and Yong Zhou",

note = "Publisher Copyright: {\textcopyright} 2015 Elsevier B.V.",

year = "2015",

month = mar,

day = "1",

doi = "10.1016/j.spl.2014.12.006",

language = "英语",

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T1 - A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator

AU - Qiu, Zhiping

AU - Chen, Xiaoping

AU - Zhou, Yong

PY - 2015/3/1

Y1 - 2015/3/1

N2 - In this paper, a nonparametric imputation method is developed for the additive hazards model when the censoring indicator is missing at random (MAR). The asymptotic properties of the proposed estimator are derived and the performance of the proposed estimator is demonstrated by a numerical simulation.

AB - In this paper, a nonparametric imputation method is developed for the additive hazards model when the censoring indicator is missing at random (MAR). The asymptotic properties of the proposed estimator are derived and the performance of the proposed estimator is demonstrated by a numerical simulation.

KW - Additive hazards model

KW - Imputation

KW - Kernel smoothing

KW - Missing at random

KW - Missing data

UR - https://www.scopus.com/pages/publications/84920903639

U2 - 10.1016/j.spl.2014.12.006

DO - 10.1016/j.spl.2014.12.006

M3 - 文章

AN - SCOPUS:84920903639

SN - 0167-7152

VL - 98

SP - 89

EP - 97

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

ER -

A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator

Abstract

Keywords

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