Composite estimating equation approach for additive risk model with length-biased and right-censored data

Huijuan Ma; Feipeng Zhang; Yong Zhou

doi:10.1016/j.spl.2014.08.021

Composite estimating equation approach for additive risk model with length-biased and right-censored data

Huijuan Ma^*, Feipeng Zhang, Yong Zhou

^*Corresponding author for this work

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

19 Scopus citations

Abstract

We develop composite estimators and its large sample properties for the additive risk model with length-biased and right-censored data. We also conduct simulation studies to confirm the good finite sample performance of our methods and then give a real data example.

Original language	English
Pages (from-to)	45-53
Number of pages	9
Journal	Statistics and Probability Letters
Volume	96
DOIs	https://doi.org/10.1016/j.spl.2014.08.021
State	Published - 1 Jan 2015
Externally published	Yes

Keywords

Additive risk model
Composite estimating equation
Left truncation
Length-biased data

Access to Document

10.1016/j.spl.2014.08.021

Cite this

@article{f87f304ff4814660a211cce952da2c6c,

title = "Composite estimating equation approach for additive risk model with length-biased and right-censored data",

abstract = "We develop composite estimators and its large sample properties for the additive risk model with length-biased and right-censored data. We also conduct simulation studies to confirm the good finite sample performance of our methods and then give a real data example.",

keywords = "Additive risk model, Composite estimating equation, Left truncation, Length-biased data",

author = "Huijuan Ma and Feipeng Zhang and Yong Zhou",

note = "Publisher Copyright: {\textcopyright} 2014 Elsevier B.V.",

year = "2015",

month = jan,

day = "1",

doi = "10.1016/j.spl.2014.08.021",

language = "英语",

volume = "96",

pages = "45--53",

journal = "Statistics and Probability Letters",

issn = "0167-7152",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Composite estimating equation approach for additive risk model with length-biased and right-censored data

AU - Ma, Huijuan

AU - Zhang, Feipeng

AU - Zhou, Yong

PY - 2015/1/1

Y1 - 2015/1/1

N2 - We develop composite estimators and its large sample properties for the additive risk model with length-biased and right-censored data. We also conduct simulation studies to confirm the good finite sample performance of our methods and then give a real data example.

AB - We develop composite estimators and its large sample properties for the additive risk model with length-biased and right-censored data. We also conduct simulation studies to confirm the good finite sample performance of our methods and then give a real data example.

KW - Additive risk model

KW - Composite estimating equation

KW - Left truncation

KW - Length-biased data

UR - https://www.scopus.com/pages/publications/84907789926

U2 - 10.1016/j.spl.2014.08.021

DO - 10.1016/j.spl.2014.08.021

M3 - 文章

AN - SCOPUS:84907789926

SN - 0167-7152

VL - 96

SP - 45

EP - 53

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

ER -

Composite estimating equation approach for additive risk model with length-biased and right-censored data

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this